A product of AIPE · Grounded research engine

Research that shows its work.

inquiry turns the open web — and your own sources — into typed, cited, structured knowledge: every field traced to the sentence that supports it, organized by an ontology it derives and versions. It grounds, it governs. It refuses to fabricate.

Pipeline
probe · inquiry · graph · agents
Stages
Acquire · Understand · Ground · Govern
Output
Typed · Cited · Versioned
Status
v1 live · loop in design
Record 02 · leads pack · grounded Cited
  1. Source
  2. Extract
  3. Ground
  4. Cite

Every field traced to the sentence that supports it. Unsupported values are dropped at the grounding gate — not guessed.

§ 01 · What it is

A research engine that refuses to guess.

inquiry is a grounded deep-research engine. Give it a question and it discovers a population, derives a typed ontology for the topic, extracts every source against it, and grounds each value in the text that supports it. The ontology is versioned and reusable — a durable schema you re-query, not a throwaway prompt. It is governed so it won’t fabricate.

Grounded output

Provenance
Per-field · source-linked
Fabrication
Dropped at the grounding gate
Governance
Fill · grounding · discrimination
Output
Typed records + silver Document

Reusable ontology

Schema
Derived per topic, not prompted
Version
Content-hash · reproducible
Drift
Re-derive → diff what changed
Reuse
Re-query the same dataset

§ 02 · The inversion

One-shot AI gives an answer. inquiry gives an asset.

A chat model answers a research question from its training soup and asks you to trust it. inquiry answers from a population it actually built and canonicalized — and hands you the schema, the records, and the provenance, so you can re-run it, re-query it, and audit every line.

One-shot chat research

  • An answer you cannot reproduce
  • Citations you have to take on faith
  • A schema thrown away after every run
  • Runs only in someone else’s cloud

inquiry

  • Grounded, cited, auditable — per field
  • A versioned ontology you keep and re-query
  • A self-correcting loop, human in the loop
  • MCP-native · on-prem · configurable

§ 03 · The method

Acquire. Understand. Improve. Deliver.

  1. 01

    Acquire

    probe crawls and renders the open web — and your own sources — into content-addressed, replayable captures. inquiry never crawls; it reads the lake.

  2. 02

    Understand

    Derive a per-topic ontology from sample captures, extract every source against it, ground each value to the text, and govern the result gold-free.

  3. 03

    Improve

    A judge scores the schema against the evidence it produced and emits typed deficits; a repair loop refines it, escalating to a human when it can’t.

  4. 04

    Deliver

    A typed, cited dataset plus a grounded narrative — queryable, versioned, and exposed to any agent over MCP.

  • Grounding gate
  • Gold-free governance
  • Content-hash versioning
  • Drift refresh
  • On-prem local models

§ 04 · Where it earns its keep

Research you run more than once.

inquiry is built for the domains a business researches repeatedly — where the valuable artifact is a living, governed dataset, not a one-off answer. Each run is cheaper and better than the last, and re-running it tells you what changed.

No.Use caseOutputRefreshEvidence
01Company & lead enrichmentFused firmographic recordon changegrounded ↗
02Market & competitor researchGoverned dataset + narrativeweeklygrounded ↗
03Entity dossiers / backgroundTyped profile, every claim sourcedon demandgrounded ↗
04Continuous monitoringDrift-refresh: diff what changedscheduledgrounded ↗

MCP-native — drop inquiry into any agent. On-prem and private: it runs local models, no data egress. Configurable per stage, with pluggable search backends.

§ 05 · In good faith

What ships today, and what’s next.

The grounded core is live: capture → derive ontology → extract → ground → govern → persist, proven in production on coffee.church. Reasoning runs on-prem on a local model, so page text never leaves the cluster. The self-improving loop — a deficit-emitting judge, a repair planner, and a human-in-the-loop review gate — is a designed roadmap, not a finished claim. We say which is which.


Start with one question.

Bring a domain you research repeatedly. We derive the ontology, run the population, and hand you a governed dataset with the provenance to prove it.