Lomita Documentation

Lomita is a decision framework. Ask any research question and get a statistically rigorous, data-backed answer — across internal and external data, automatically.

What Lomita does

• Finds the data you need — 104+ pre-built sources, plus AI agents that search the internet for APIs and datasets you don't know exist
• Builds the pipeline automatically — agents read API documentation, handle authentication, and start ingesting data without manual setup
• Runs rigorous analysis — statistical tests with confidence intervals, not guesses. Correlations, regressions, Granger causality, regime analysis.
• Delivers a verdict — SUPPORTED, REFUTED, or INCONCLUSIVE — with the methodology to prove it
• Builds your knowledge graph — every hypothesis, correlation, and data source compounds into an organizational decision map

Two ways to use Lomita

From your browser — sign in at lomita.io, go to the Explore page, and ask questions in the built-in chat. The knowledge graph grows as you research.

From your AI agent — connect Claude Code, Claude Desktop, or any MCP-compatible agent for deeper research, scripted workflows, and programmatic access.

Quick links

• Quickstart — sign up to first research in 5 minutes
• Using the Chat — the primary research interface
• Data Discovery — how agents find and connect new data
• Connecting Your Own Data — bring your CRM, ERP, or any API
• Tools Reference — every tool explained

Who it's for

Decision-makers who need data-backed answers but don't have a data science team. CEOs validating market hypotheses. CMOs measuring campaign effectiveness against real benchmarks. Founders making resource allocation decisions with evidence, not gut feel.

You don't need to know SQL, Python, or data engineering. You need a question worth answering.

Pricing

• Standard ($640/mo) — 3 team members, 104+ data sources, autonomous research agents, email + webhook delivery
• Pro ($990/mo) — 10 team members, priority agent execution, advanced monitoring

Each instance is a dedicated container. No shared resources. Cancel anytime.

Lomita is a product of Plantos Technologies, Inc.

What is Lomita?

Lomita is a decision framework that finds, integrates, and analyzes data so you can validate assumptions before committing resources.

The problem

Every company makes decisions on incomplete data. You validate a market hypothesis by asking an analyst who queries one database. You measure campaign ROI against a single attribution model. You optimize operations based on internal metrics without external context.

Each decision is made within a silo — one or two dimensions of data — because finding, integrating, and analyzing additional sources is prohibitively expensive and slow.

The result: decisions backed by gut feel dressed up as data. Internal data tells you what happened. It rarely tells you why.

The solution

State a hypothesis. Lomita finds the data, runs the analysis, and delivers a verdict.

The product is not a pre-built data warehouse. The 104 pre-built sources are a starting point. The actual product is the ability to:

1. Discover data that doesn't exist in your system yet — AI agents search the internet for relevant APIs, datasets, and services
2. Integrate any data source automatically — agents read API documentation, build data pipelines, handle authentication, and start ingesting
3. Analyze across all dimensions — internal data (your CRM, your ERP) combined with external data (macro indicators, sentiment, weather, competitors)
4. Validate or refute with statistical rigor — not dashboards, not gut feel. Statistical tests with confidence intervals and methodology you can audit.
5. Build a persistent knowledge graph — every hypothesis, every correlation, every data connection compounds. The system gets smarter with every question.

How it works

You: "Does ERCOT energy demand correlate with Dallas weather extremes?"

Lomita:
  1. Discovery Agent searches 104+ sources + the internet for ERCOT and weather data
  2. Integration Engineer builds data pipelines for ERCOT, NOAA, EIA
  3. Quant Analyst runs Pearson correlations, Granger causality, regime analysis
  4. Research Narrator compiles an executive report with verdict

Result: SUPPORTED (r = 0.67, n = 8,760, Granger p < 0.01)
        Report auto-delivered to your email and Slack.

Two ways to use Lomita

Browser chat (easiest) — Sign in, go to Explore, type a question. The knowledge graph grows in real time as agents work. Click any hypothesis to see connected data, correlations, and reports.

AI agent via MCP — Connect Claude Code, Claude Desktop, or any MCP-compatible agent for deeper research, scripted workflows, and programmatic access. One config snippet, no API key setup required.

Who it's for

Decision-makers who understand that decisions should be backed by data — but don't have the technical skills or headcount to do rigorous analysis themselves.

• CEOs — "Is this market actually growing, or are we riding our own momentum?"
• CMOs — "Is our spend working, or is the market lifting everyone?"
• Founders — "Should we expand into this vertical? What does the data say?"
• Operations leaders — "Is this supply chain issue specific to us, or industry-wide?"
• PE/VC analysts — "Is this company's growth organic, or riding macro tailwinds?"

At $640/month, Lomita replaces the $150K/year hire you can't justify.

Pricing

Each instance is a dedicated container. No shared resources, no noisy neighbors. Cancel anytime.

Get started in 5 minutes →

Quickstart

Sign up, ask a question, get a data-backed answer. Under 5 minutes.

1. Create an account

Go to lomita.io and sign in with Google, GitHub, Apple, or email.

2. Launch an instance

Click New Instance, pick your tier (Standard $640/mo or Pro $990/mo), and complete payment. Your dedicated container provisions automatically — about 3 minutes.

The dashboard shows "Provisioning..." with a spinner. When ready, the status changes to Active.

3. Start researching

You have two options:

Option A: Use the built-in chat (easiest)

Click Explore in the sidebar. Type your question in the chat at the bottom:

Research whether oil prices predict inflation

That's it. The agent team starts working immediately:

1. Discovery Agent finds relevant data sources
2. Integration Engineer builds data pipelines
3. Quant Analyst runs statistical analysis
4. Research Narrator writes an executive report

Watch the Hypotheses panel on the right — your research appears with a pulsing dot while agents work. When it turns green, yellow, or red, the verdict is in.

Option B: Connect your AI agent (for power users)

Go to Connect in the sidebar. Copy the config for your framework:

Claude Code:

claude mcp add --transport http lomita https://YOUR-INSTANCE-mcp.lomita.io/mcp

Your browser opens for authentication — no API key needed. Then ask:

Research whether consumer sentiment predicts stock market volatility

Claude Desktop: Download the config from the Connect page and import it.

Other frameworks: Any MCP-compatible agent works. Copy the JSON config from the Connect page.

4. Get your report

When research completes:

• On the Explore page — click "View Report" next to the hypothesis
• By email — tell the chat: "Deliver this report to [email protected]"
• To Slack — tell the chat: "Deliver this to our Slack channel" and provide the webhook URL
• Via your AI agent — deliver(email: "[email protected]")

Reports include: executive summary, key findings with statistics, data sources used, methodology, limitations, and a clear verdict.

5. Keep going

• Ask follow-up questions with the hypothesis selected
• Click nodes on the knowledge graph to explore connections
• Connect your own data sources (CRM, ERP, any API) from the Sources page
• Set up continuous monitoring: "Monitor this weekly and alert me if anything changes"

No LLM setup required

Lomita includes a bundled AI model for all agent operations. You don't need to bring your own API key — research works immediately after connecting.

Advanced users can optionally override the default model with their own provider (Anthropic, OpenAI, Google Gemini) via the set_provider tool.

Using the Chat

The Explore page is the primary interface. It combines a knowledge graph, a hypothesis panel, and a chat — all in one view.

The layout

• Knowledge graph — the main canvas. Every data source, entity, and hypothesis is a node. Correlations and connections are edges. The graph grows as you research.
• Hypothesis panel (right side) — all your research questions with status. Click one to filter the graph to its connected data.
• Chat (bottom) — your research assistant. Ask questions, create hypotheses, request deliveries, analyze data.

How to use the chat

Type any question or command:

Hypothesis panel

When you click a hypothesis in the right panel:

• The graph fades unrelated nodes and highlights connected data
• The chat gains context — it knows which hypothesis you're investigating
• The chat placeholder changes to "Ask about this hypothesis..."
• You can say "deliver this" or "what did you find?" without specifying which research

Click Show all to restore the full graph view.

Status indicators

Tips

• Shift+click nodes on the graph to multi-select and ask about relationships
• Search the graph using the search bar to find specific entities
• Click View Report on any completed hypothesis to see the full analysis
• The graph polls every 15 seconds — new nodes appear automatically as agents discover data

Connecting an AI Agent

The built-in chat on the Explore page handles most research needs. Connecting an external AI agent gives you additional power: scripted workflows, batch operations, and integration with your existing development tools.

Claude Code

Copy the command from the Connect page in your dashboard:

claude mcp add --transport http lomita https://YOUR-INSTANCE-mcp.lomita.io/mcp

Your browser opens for sign-in — no API key copy-paste needed. Start a new Claude Code session and Lomita tools are available.

Claude Desktop

Download the pre-filled config from the Connect page, or add manually:

Settings > Developer > Edit Config:

{
  "mcpServers": {
    "lomita": {
      "type": "http",
      "url": "https://YOUR-INSTANCE-mcp.lomita.io/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Get your API key from Settings in the dashboard.

OpenCode / Other MCP frameworks

Use the JSON config from the Connect page. Any framework that supports HTTP MCP transport works:

{
  "type": "http",
  "url": "https://YOUR-INSTANCE-mcp.lomita.io/mcp",
  "headers": {
    "Authorization": "Bearer YOUR_API_KEY"
  }
}

What your agent can do

Once connected, your agent has access to these tools:

No LLM setup required

The research agent team uses a bundled AI model. You don't need to configure a provider — research works immediately.

If you want to use your own model (Anthropic, OpenAI, Gemini), you can optionally override with set_provider. This is not required.

Asking Research Questions

State a hypothesis. Lomita finds the data, runs the analysis, and delivers a verdict.

What happens when you ask a question

When you type "Research whether ERCOT energy demand correlates with Dallas weather" — either in the chat or via your AI agent — here's what happens behind the scenes:

1. Discovery (30-60 seconds)

The Discovery Agent searches for relevant data:

• Scans the 104-source catalog for matches (ERCOT, NOAA weather, EIA energy)
• Searches the internet via Exa for APIs not in the catalog
• Connects the best sources to your hypothesis
• Documents any gaps (e.g., "Uber ride data not publicly available — using TomTom traffic as proxy")

2. Integration (1-2 minutes)

The Integration Engineer builds data pipelines:

• Reads API documentation for each source
• Writes and deploys ingestion scripts (Dagster assets)
• Handles authentication and scheduling
• Verifies data is flowing into the data lake

3. Analysis (1-2 minutes)

The Quant Analyst runs statistical tests:

• Pearson and Spearman correlations
• Granger causality (does X predict Y?)
• Lag analysis (how many days/weeks of lead time?)
• Regime analysis (does the relationship change in different conditions?)
• OLS regression with confidence intervals

4. Report (30-60 seconds)

The Research Narrator compiles findings:

• Executive summary with a clear verdict
• Key findings table with statistics
• Data sources used with coverage dates
• Methodology description
• Limitations and caveats
• Recommendation

Total time: 3-10 minutes depending on complexity.

Verdicts

Every verdict comes with the methodology to prove it. You can audit the statistics, check the data sources, and verify the reasoning.

Follow-up research

After a hypothesis completes, you can:

• Follow up — "Break this down by season" creates a child hypothesis that branches from the original
• Deliver — Send the report to email, Slack, or any webhook
• Monitor — "Watch this weekly and alert me if the correlation shifts"
• Connect more data — "Connect my internal sales data and re-analyze"

Tips for good questions

Good questions (specific, testable):

• "Does consumer sentiment predict retail sales with a lag?"
• "Are ERCOT energy prices affected by Dallas weather extremes?"
• "Does VIX volatility correlate with S&P 500 drawdowns?"

Weaker questions (too broad):

• "Tell me about the economy" — too vague, no testable hypothesis
• "What should I invest in?" — Lomita analyzes data, it doesn't give investment advice

The system works best with questions that have a clear independent variable and dependent variable.

Data Discovery

Lomita's primary capability is finding data you didn't know existed. The 104 pre-built sources are a starting point — the real product is the discovery engine.

How discovery works

When you create a research hypothesis, the Discovery Agent follows a three-step strategy:

Step 1: Search the catalog

The agent searches 104 pre-built data sources across 8 domains:

• Macro & Economics (FRED, BLS, Census, Treasury)
• Energy (EIA, ERCOT, Baker Hughes)
• Sentiment (Reddit, Polymarket)
• Commodities (CFTC, USDA, World Bank)
• Real Estate (Zillow, MTA)
• Transportation (TomTom, FAA)
• Weather (NOAA, NWS)
• Frontier (DeFi, crypto)

If the catalog has what you need, the agent connects the sources immediately.

Step 2: Search the internet

If the catalog doesn't cover your topic, the agent searches the internet using Exa for:

• Public APIs that provide the data
• Government datasets and open data portals
• Free data services with API access

When the agent finds a potential source, it creates a custom integration and the Integration Engineer builds a pipeline automatically.

Step 3: Connect custom / internal sources

For private data sources (your CRM, ERP, internal APIs):

• Tell the chat: "Connect my HubSpot" or "Connect our internal sales API"
• Provide API credentials when asked
• The Integration Engineer reads the API docs and builds the pipeline
• Credentials are encrypted with AES-256-GCM and stored securely

Intelligent substitution

When exact data isn't available, the Discovery Agent finds proxies:

• No Uber ride data? → Uses TomTom traffic congestion as a mobility proxy
• No direct competitor pricing? → Uses industry benchmark data from public filings
• Historical data unavailable? → Documents the gap and works with what's available

The agent always documents its reasoning — you can see why it chose specific sources and what gaps exist.

Your data stays yours

• Each tenant has an isolated data lake (MinIO S3)
• Scoped credentials prevent cross-tenant data access
• Custom integrations are encrypted at rest
• Data you connect is only visible to your team

The Knowledge Graph

Every hypothesis, correlation, and data source builds a persistent knowledge graph. Over time, this becomes your organization's decision map — a living record of what you've investigated and what the data says.

What's in the graph

How it grows

The graph starts with 104 data source nodes (the catalog). As you research:

1. Ask a question → a Hypothesis node appears
2. Agents discover data → Source nodes connect to the hypothesis
3. Analysis runs → Entity nodes are created from correlations
4. Correlations found → CORRELATES_WITH edges with r-values connect entities
5. More research → the graph densifies. Connections between past and current research emerge.

Why it matters

A CEO asked about interest rates and mortgage applications 3 months ago. Today, someone asks about housing starts and lumber prices. The graph already knows these are connected — interest rates link both investigations. The system doesn't re-discover what it already knows.

This is the anti-silo architecture. Marketing's research connects to Operations' research connects to Finance's research — through shared entities and data sources, not departmental boundaries.

Using the graph

• Click a hypothesis in the right panel to filter the graph to its connected nodes
• Shift+click nodes to multi-select and ask about relationships
• Search to find specific entities by name
• Hover to see node labels and types
• The graph polls every 15 seconds — new nodes appear as agents work

Report Delivery

Research is only useful if it reaches the people who make decisions. Lomita delivers reports to email, Slack, and any webhook endpoint.

Delivering a report

From the chat

With a hypothesis selected, say:

Deliver this report to [email protected]

Send this to our Slack channel

(Provide the Slack webhook URL when asked)

From your AI agent

deliver(email: "[email protected]", hypothesis_id: "abc-123")

deliver(webhook_url: "https://hooks.slack.com/services/...", hypothesis_id: "abc-123")

Without specifying a hypothesis

If you don't specify a hypothesis ID, Lomita delivers the most recently completed report.

Supported channels

Auto-delivery

When research completes, the Research Narrator automatically delivers the report to the account email on file. You don't need to ask — it arrives in your inbox.

Viewing reports

Reports are also stored in your research repository:

https://YOUR-INSTANCE-git.lomita.io/lomita/research

Click View Report on any hypothesis in the dashboard or explore page to go directly to the report.

Report format

Every report follows the same structure:

1. Question — the original hypothesis in plain language
2. Answer — 1-2 sentence verdict with key numbers
3. Key Findings — table of results with confidence levels
4. Data Sources — what data was used, date ranges, observation counts
5. Methodology — statistical methods, sample sizes, confidence intervals
6. Limitations — caveats and data quality notes
7. Recommendation — actionable next step based on findings

Continuous Monitoring

Set a hypothesis to re-analyze on a schedule. Get alerts when correlations shift, trends reverse, or your data signals change.

Setting up monitoring

From the chat

Monitor this weekly and send updates to [email protected]

From your AI agent

monitor(hypothesis_id: "abc-123", frequency: "weekly")

How it works

1. You enable monitoring on a completed hypothesis
2. On schedule (daily, weekly, or monthly), the Pipeline Monitor agent checks for due hypotheses
3. The agent team re-runs the analysis against the latest data
4. A delta report is generated: what changed, what stayed the same
5. The report is delivered to all configured channels (email, Slack, webhook)

What you receive

When nothing changed

MONITORING UPDATE: STABLE

Your hypothesis "Do interest rates affect mortgage applications?" 
remains SUPPORTED. No significant changes detected since last check.

Key metrics:
- Correlation: r = 0.67 (was 0.67) — unchanged
- Trend direction: still negative

The system always reports — even stable findings. Absence of change is a valuable signal.

When something changed

MONITORING UPDATE: STATUS CHANGED (Green → Yellow)

Your hypothesis "Do interest rates affect mortgage applications?"
has shifted from SUPPORTED to INCONCLUSIVE.

What changed:
- Correlation weakened: r = 0.67 → r = 0.34
- New data from Q2 shows divergence in the relationship

Status changes trigger an immediate alert outside the regular schedule.

Frequencies

Disabling monitoring

Stop monitoring this hypothesis

Or via your agent:

monitor(hypothesis_id: "abc-123", enabled: false)

Previous reports are preserved — only future checks stop.

Data Sources Overview

Lomita includes 104 pre-built data sources across 8 domains — but this is just the starting point. AI agents can also search the internet for sources not in the catalog and connect your private data (CRM, ERP, APIs). See Connecting Your Own Data.

Categories

Macro & Economics (27 sources)

FRED series covering rates, yields, spreads, monetary policy, and economic indicators.

Full macro source list →

Sentiment & Markets (10 sources)

Reddit, Polymarket, and market sentiment indicators.

Energy (8 sources)

EIA, ERCOT, and energy market data.

Frontier / DeFi (5 sources)

Crypto, DeFi, and emerging data.

Transportation (4 sources)

Traffic, flights, and border crossings.

Commodities (3 sources)

CFTC, USDA, and World Bank data.

Weather (4 sources)

NOAA, NWS, and weather data.

Real Estate (3 sources)

Google Places, MTA, and property data.

How discovery works

Your agent calls discover_sources("your keywords") to search the catalog:

Agent: discover_sources("consumer sentiment")
→ Returns: fred-umcsent, reddit-retail-sentiment, alphavantage-sentiment, ...

Each source includes:

• Name and description
• Data domain and tags
• Update frequency
• Whether credentials are required
• Sample fields

Data format

• Raw data is stored as JSON in s3://plantos-raw/{source}/{date}/
• Transformed data is stored as Parquet in s3://plantos-analytics/marts/
• Query both using analyze_data with SQL

Custom sources

You can upload your own data:

"Upload my portfolio CSV to the data lake"
→ upload_data("portfolio.csv")
→ Returns a presigned URL for upload

Or create custom Dagster assets for new API sources:

"Create a custom asset to scrape gold prices from the LBMA API"
→ propose_asset("gold_lbma_price", python_code, description)
→ Opens a PR for review

Connecting Your Own Data

Lomita works with any data source — not just the 104 in the catalog. Connect your CRM, ERP, internal APIs, databases, or upload files. AI agents handle the integration automatically.

From the chat

Tell the chat what you want to connect:

Connect my HubSpot — I need deal pipeline and close dates

Connect our internal sales API at api.company.com

The Discovery Agent will ask for:

• API documentation URL (helps the agent understand endpoints)
• Authentication type (API key, OAuth, bearer token)
• Credentials (encrypted with AES-256-GCM, stored securely)
• What specific data you need

The Integration Engineer then reads the docs, builds a Dagster data pipeline, and starts ingesting data — all automatically.

From your AI agent

connect_integration(
  service: "hubspot",
  credentials: { api_key: "pat-na1-..." },
  config: {
    goal: "Pull deal pipeline data — amounts, stages, close dates",
    docs_url: "https://developers.hubspot.com/docs/api"
  }
)

From the Sources page

Click Connect via Form on the Sources page to fill in:

• Service name
• API credentials (key-value pairs)
• What data you need (plain English)
• API documentation URL (optional, helps the agent)
• Sync schedule (hourly, daily, weekly)

What happens next

1. Credentials are encrypted and stored
2. The Integration Engineer receives a task
3. The agent reads the API documentation
4. A Dagster data pipeline is created and deployed
5. Data starts flowing into your private data lake
6. The source becomes available for all future research

Uploading files

For one-off data (CSV, JSON, Parquet):

upload(filename: "sales-q1.csv", content: "...")

Or use the Sources page upload feature. Uploaded data is queryable immediately via the analytics engine.

Security

• Credentials encrypted with AES-256-GCM at rest
• Decrypted only at pipeline runtime by the Integration Engineer
• Stored in your tenant's isolated PostgreSQL database
• Never shared across tenants
• API key rotation supported via the Sources page

Example: ERCOT Energy vs Dallas Weather

A real research investigation run on Lomita, demonstrating data discovery, agent chain execution, and statistical analysis.

The question

"Does ERCOT energy demand correlate with Dallas weather extremes?"

What happened

Time: ~5 minutes from question to completed report

Discovery Agent (30 seconds)

Searched the catalog and found:

• ercot-public-api — ERCOT locational marginal pricing and grid load data
• noaa-cdo-weather — Historical temperature and weather for Dallas
• eia-electricity-generation — Real-time electricity generation by fuel type
• eia-steo-forecasts — Short-term energy outlook

Integration Engineer (2 minutes)

Built data pipelines for each source. Three Integration Engineers ran in parallel — each building a separate pipeline.

Quant Analyst (2 minutes)

Ran statistical analysis:

• Pearson correlation between temperature and ERCOT load
• Granger causality tests (does temperature predict demand?)
• Lag analysis (how many hours of lead time?)
• Regime analysis (does the relationship differ in summer vs winter?)

Research Narrator (1 minute)

Compiled findings into an executive report.

The verdict

SUPPORTED (Green)

Key findings:

• Strong positive correlation between temperature extremes and ERCOT load (r = 0.67)
• Bidirectional Granger causality (p < 0.01) — temperature predicts demand
• Strongest effect at 1-hour lag in summer (AC load)
• Winter heating demand also significant but weaker

What the graph shows

After this research completed, the knowledge graph showed:

• Hypothesis node (green) connected to 4 source nodes
• Entity nodes: "Dallas Temperature", "ERCOT Load", "ERCOT LZ_NORTH Price"
• CORRELATES_WITH edges with r-values between entities
• Cross-domain connections (weather → energy) visible in the graph layout

Delivery

The report was auto-delivered to email and manually delivered to Slack — both via the chat:

Deliver this report to [email protected]

Try it yourself

This exact research can be reproduced on any Lomita instance. Go to the Explore page and type:

Research whether ERCOT energy demand correlates with Dallas weather extremes

Example: Treasury Yields vs VIX Volatility

This example shows a real research run on the Lomita platform. The entire process — from question to published report — ran autonomously.

The question

"Do rising 10-year Treasury yields predict VIX volatility changes?"

What happened

research("Does rising 10-year Treasury yields predict VIX volatility changes?")

The research team executed automatically:

1. Found FRED DGS10 (10-Year Treasury) and FRED VIXCLS (CBOE Volatility Index) — 9,084 daily observations from 1990 to 2026.

2. Verified data freshness and confirmed both series were aligned.

3. Ran 7 statistical tests:

   • Pearson correlation (levels and changes)
   • 2-week lag analysis (the critical test)
   • Linear regression
   • Regime analysis (high-yield vs low-yield environments)
   • Structural break test (pre-2008 vs post-2008)

4. Compiled the findings into an executive report.

The result

REFUTED. The 2-week lag correlation is r = +0.009 — effectively zero.

Key insight

Even the strongest observed relationship (change-on-change r = -0.157) explains only 2.5% of variance. The regime analysis found an interesting sign reversal — in high-yield environments the correlation flips positive — but it's not reliable enough to be actionable.

Time to complete

The full analysis ran across several cycles (approximately 15-20 minutes from question to published report).

Report location

The full report with analysis artifacts was published to:

git.YOUR-INSTANCE.lomita.io/lomita/research/dgs10-vix-statistical-analysis/report.md

Try it yourself

After connecting your agent:

Set provider to zen with key YOUR-ZEN-KEY
Research whether consumer sentiment predicts retail sales with a 1-month lag

Tools Reference

When you connect your AI agent to Lomita, you see 9 tools. These are all you need — your research team handles everything else behind the scenes.

Your Tools

How to use them

research

The primary tool. Ask any research question and the research team does the rest.

"Research whether consumer sentiment predicts retail sales growth"

What happens behind the scenes:

1. Formalizes your research question
2. Finds relevant data from 104+ sources
3. Prepares and connects the data
4. Runs statistical tests (correlation, lag analysis, regression, regime analysis)
5. Compiles an executive report
6. Report is published to your research repository

You get back a Research ID. Use status to track progress.

follow_up

Branch from an existing investigation to explore a related question.

"Follow up on research abc-123: break this down by pre-2020 vs post-2020"

Creates a follow-up investigation linked to the original, then runs the same research process.

status

Check progress on your research. Call with no arguments to see everything, or with a Research ID for details.

"Check the status of my research"
"What's the status of research abc-123?"

Returns: research status, connected data sources, recent activity, and a link to the report when complete.

sources

Search the data catalog to see what's available before starting research.

"What data sources do you have for energy markets?"
"Search for sentiment data"

Returns matching sources with descriptions, domains, and whether authentication is required.

upload

Add your own proprietary data to the data lake. Returns a presigned upload URL.

"Upload my-sales-data.csv"

Your data is stored privately and becomes available for analysis alongside the 104 public sources.

monitor

Set up continuous monitoring on a research question so it is automatically re-analyzed on a schedule.

"Monitor research abc-123 weekly and send results to my email and Slack"

Parameters:

How monitoring works:

• On each scheduled cycle, the research team re-runs the full analysis against the latest data.
• Reports are always delivered — even when findings are stable. Absence of change is valuable information.
• If a research status flips (e.g., SUPPORTED to INCONCLUSIVE), an immediate alert is sent outside the normal schedule.
• Updated reports are committed to your research repository as new versions. Use git diff to see exactly what changed between cycles.

To disable monitoring:

"Stop monitoring research abc-123"

See Continuous Monitoring for a full walkthrough.

deliver

Send a completed report to email, a webhook, or both. Auto-detects the platform from the webhook URL.

"Deliver report abc-123 to [email protected]"
"Deliver report abc-123 to https://hooks.slack.com/services/T00/B00/xxx"
"Deliver report abc-123 to [email protected] and https://discord.com/api/webhooks/123/abc"

Supported destinations:

The platform is auto-detected from the URL — no extra configuration needed. You can provide both an email and a webhook URL in a single call.

set_provider

Configure the LLM that powers your research agents. This is typically the first thing you do after connecting.

"Set provider to zen with key sk-abc123..."
"Configure anthropic with my API key"

Supported providers: Anthropic, OpenAI, Google (Gemini), DeepSeek, Zen (OpenCode), Groq, Mistral, xAI, OpenRouter, Cerebras.

Zen tiers:

To use the unlimited tier:

"Set provider to zen-go with my subscription key"

When you set a provider, your research team is automatically configured and ready to work.

list_providers

See which LLM providers you've configured.

"List my configured providers"

pause_agents

Pause all research agents to stop LLM token consumption. Useful when you're done for the day or want to review results before continuing.

"Pause all agents"

Any in-progress work is suspended gracefully. Agents can be resumed at any time.

resume_agents

Resume previously paused agents to continue research.

"Resume agents"

Agents pick up where they left off.

What you don't see

Behind your 9 tools, there are 44+ internal tools that the research team uses. These handle data connections, SQL queries, code review, task management, and coordination. You never need to touch them — the research tool orchestrates everything automatically.

Reports

When research completes, the report is published to your research repository:

https://YOUR-INSTANCE-git.lomita.io/lomita/research

Each investigation gets its own directory with:

• report.md — the executive report (rendered as formatted markdown)
• analysis/ — SQL queries and Python scripts used
• data-sources.md — what data was used

The repository's README shows an auto-generated index of all your research with status and links.

FAQ

How much does it cost?

No additional API key or LLM costs — the research agent team's AI model is included.

Do I need to set up an AI provider?

No. Lomita includes a bundled AI model for all agent operations. Research works immediately after connecting — no API key setup required.

Advanced users can optionally override the default model with their own provider (Anthropic, OpenAI, Google Gemini) via the set_provider tool.

Do I need an AI agent to use Lomita?

No. The built-in chat on the Explore page is the primary interface. You can ask research questions, connect data sources, and receive reports entirely from your browser.

Connecting an external AI agent (Claude Code, Claude Desktop, etc.) is optional — it gives you programmatic access, scripted workflows, and deeper integration.

Can Lomita find data I don't have?

Yes. This is the primary product capability. When you ask a question, AI agents:

1. Search the 104-source catalog
2. Search the internet for APIs and datasets not in the catalog
3. Connect to your private data sources (CRM, ERP, APIs) if you provide credentials

The agents build data pipelines automatically — you don't need to know where the data lives.

Can I connect my own data?

Yes. Tell the chat "Connect my HubSpot" or go to the Sources page. Provide credentials and describe what data you need. The Integration Engineer reads the API docs and builds the pipeline.

Supported: any REST API, CRM, database, or file upload (CSV, JSON, Parquet). Credentials are encrypted with AES-256-GCM.

See Connecting Your Own Data.

Can I deliver reports to Slack, email, or Teams?

Yes. From the chat:

Deliver this report to [email protected]

Send this to our Slack channel (provide webhook URL)

Supported: Email (Resend), Slack, Discord, Microsoft Teams, Zulip, and any webhook endpoint.

Reports are also auto-delivered to your email when research completes.

How does monitoring work?

Enable monitoring on any completed hypothesis:

Monitor this weekly and alert me if anything changes

Lomita re-runs the analysis on schedule (daily, weekly, monthly) and delivers delta reports. You always get a report — even when nothing changed. If the verdict flips (e.g., SUPPORTED to INCONCLUSIVE), you get an immediate alert.

See Continuous Monitoring.

Where are my reports?

Reports are accessible from:

• Overview dashboard — click "View Report" on any completed hypothesis
• Explore page — click "View Report" in the hypothesis panel
• Research repository — https://YOUR-INSTANCE-git.lomita.io/lomita/research
• Email — auto-delivered on completion

Can my team access the same instance?

Yes. Standard supports 3 team members, Pro supports 10. Invite teammates from the Team page — they get their own API key and can access the same research, data sources, and knowledge graph.

What happens if I cancel?

You keep access through the end of your billing period. Your data, reports, and research history are preserved. You can resubscribe anytime.