Custom Knowledge Agents

Your archive is your asset.

Make it answer questions.

Years of research reports, member data, editorial archives, contract libraries. Lock the value of all of it behind a chat box your team or your subscribers can actually use. No subscription. No hallucinations. No data leaving your building.

Generic AI was never going to work on your data.

You uploaded a PDF to ChatGPT. It made things up. You tried Copilot. It missed half the report. You looked at off-the-shelf “search your docs” tools. They want monthly fees forever and your data on their servers.

Your archive is too specific, too valuable, and too sensitive for any of that. It needs a system built for the shape of your data, the questions your users actually ask, and the infrastructure you actually trust.

10yr+

Of proprietary data sitting unsearchable in PDFs and shared drives

$0

Of that data leaving your infrastructure when we build it right

Weeks

From kickoff to a working pilot, not months

100%

Ownership of the code, schema, and prompts when we’re done

What we build

A knowledge agent built for the shape of your data.

Two-tier retrieval. Structured data gets queried like a database. Narrative content gets searched semantically. The LLM is instructed to refuse rather than guess, so the answer is either right or it tells you it doesn’t have it.

Natural language interface

Chat, embed, or API. Your users ask questions in plain English; the system pulls the answer from the real data.

Structured + narrative retrieval

PostgreSQL with vector search. Hard numbers come from the database. Editorial nuance comes from semantic search. No mixing them up.

Hallucination-resistant architecture

The model is given tools, not freedom. If the data doesn’t support the answer, the system says so. Every claim is traceable to its source.

Self-service ingestion

Drop a new report into the admin interface. The system extracts, validates, and indexes it. No engineer required.

Strato Doumanis

Led by Strato Doumanis

The architect and the builder. Every line of code, every prompt, every schema.

What every engagement includes

Schema design

We map your data, your users’ questions, and the fields needed to answer them.

Ingestion pipeline

Extraction from PDFs, docs, spreadsheets, or databases into a structured store.

Retrieval architecture

Database queries for facts. Vector search for narrative. LLM orchestration that knows the difference.

Interface

Chat UI, embeddable widget, or API. Whatever your users actually need.

Documentation and handoff

Your team owns and operates the system. We’re here if you want us.

How a question becomes an answer

01 / ASK

User submits a question

Plain English, in your chat, your portal, or via API. No syntax, no training required.

02 / ROUTE

The system picks the tool

Numeric question routes to the database. Narrative question routes to vector search. Mixed questions get both.

03 / RETRIEVE

Data comes back grounded

Every result is real, traceable, and from your actual content. Source citations included.

04 / RESPOND

The answer is written

Clear, cited, conversational. If the data doesn’t support an answer, the system says so. It never invents.

Why this is different

Four things off-the-shelf tools won’t do.

Your data stays put

Local servers, your cloud, your infrastructure. Nothing trained on, nothing exported, nothing shared.

Refuses to guess

If a question can’t be answered from your data, the system says so. No fabricated facts, no plausible nonsense.

Built in weeks

Pilot in 4-6 weeks. Full build in 8-12. Not “Q4 of next fiscal year.”

You own all of it

Code, prompts, schemas, workflows. Run it yourself or pay us to. Your call, not ours.

Where it works

Built for organizations whose value is locked in their archive.

Different industries, different questions, same architecture. If your team or your audience is asking questions of years of proprietary data, this works.

B2B Media & Publishing

Make your archive a subscriber benefit.

Decades of editorial, annual research reports, and industry rankings become a queryable product your subscribers actually use.

“Which top-25 furniture retailers grew their e-commerce share fastest in the last three years?”

Associations

Members get instant answers, not voicemail.

Member directories, certification requirements, regulatory guidance, conference archives. Self-service that drives renewals.

“What CE credits do I need to maintain my certification, and which sessions at the spring conference count?”

Healthcare

Clinical knowledge, locally deployed.

Protocols, procedure libraries, training curricula, regulatory guidance. Local servers; no PHI leaves the building.

“What’s our current protocol for post-procedure follow-up on patients over 60 with prior cardiac history?”

Economic Development

Beat the RFI clock.

Site selection data, demographic studies, incentive databases, asset inventories. Turn 3-day responses into 30-minute answers.

“Which sites in our region have rail access, 50+ acres, and existing 3-phase power, and what incentives stack on them?”

Trade Unions

Steward training in hours, not weeks.

Contract archives, grievance precedent, benefits documentation. Members and stewards get authoritative answers on demand.

“What does our current contract say about overtime distribution on weekend shifts, and how has it been arbitrated historically?”

Religious & Mission-Based Orgs

Doctrine, archives, and member resources, queryable.

Publication archives, doctrinal references, parish or congregation data. Make institutional memory accessible across generations.

“What has our publication written about clergy compensation reform over the last twenty years?”

Don’t see your industry? Tell us about your data. If you’ve got years of structured content sitting in PDFs, the answer is probably yes.

What questions look like

The kinds of questions your team has been asking the wrong way.

Compare member growth in our top three chapters over the last five years, and tell me which programs drove the lift.

Summarize every article we’ve published mentioning a specific manufacturer in the last decade, grouped by year.

Which of our certified providers added a second location in the last 18 months and what services did they expand into?

Show me the average gross margin and e-commerce share for the top 25 retailers in our 2023 and 2024 reports side by side.

Pull every grievance we’ve filed about overtime in the past five years and tell me how each was resolved.

Draft a response to a site selector asking about 100,000+ sq ft properties with highway access in our region.

Proof in production

A case study from a real archive.

B2B Media

Decades of furniture industry research, now answerable in seconds.

BridgeTower Media’s Furniture Today publishes the definitive annual rankings of the U.S. furniture and bedding industries. Five reports, hundreds of company profiles, years of editorial. Subscribers couldn’t search any of it. Previous attempts using ChatGPT and Copilot returned hallucinated companies and missing numbers.

We built a custom agent that separates structured data (rankings, revenues, store counts) from narrative content (editorial profiles, expansion plans, leadership commentary), routes each query to the right retrieval method, and refuses to answer when the source data won’t support it.

Client: BridgeTower Media (Furniture Today)  ·  Sector: B2B Trade Publishing

5

Annual research reports unified into one queryable source

15+

Specialized query tools built for the editorial team’s actual questions

0

Hallucinations tolerated; the system refuses rather than guesses

12wk

From kickoff to production deployment, including the proof-of-concept phase

How engagements scale

Three ways in. You pick the depth.

Every engagement starts with a paid proof-of-concept. If the pilot doesn’t prove out, we don’t move forward. No 12-month contracts, no enterprise sales theater.

Pilot

Proof of Concept

$3K – $7K

4 to 6 weeks

One data source, validated end to end.

  • Schema design for one report or dataset
  • 4 to 6 core query tools
  • Working chat demo against real data
  • Documented architecture you can scale from
  • Go / no-go decision at the end with no further commitment

Build

Full Production Deployment

$20K – $40K

8 to 12 weeks

Everything your team needs, in production.

  • Complete dataset ingestion across all sources
  • 15 to 25 specialized query tools
  • Custom chat interface or admin portal
  • Self-service ingestion for ongoing data
  • Full handoff with documentation and training

Platform

Subscriber or Member Product

$60K+

12 to 20 weeks

Multi-user, multi-source, public-facing.

  • Multi-tenant authentication and access control
  • Embeddable widget for your subscriber portal
  • Usage analytics, query logs, and content gap reporting
  • SSO and integration with your existing systems
  • Monetization-ready as a premium tier or standalone product

Ongoing support

$250 to $1,500 per month, depending on usage and uptime needs.

Or operate it yourself. You own everything we build, so the choice is genuinely yours.

michael-mckinney-vp-bridgetower-media

“I bring Strato into the work that matters most. UX, front-end, data, AI, he has a point of view on all of it, and it’s one I trust.”

Michael McKinney, Vice President of Digital Product, BridgeTower Media

FAQ

Questions we hear a lot.

ChatGPT (and Copilot, and every “chat with your PDF” tool) treats your documents as text to summarize. It hallucinates numbers, invents companies, and misses anything that requires actually counting or comparing. We extract structured fields into a database where queries are exact, and we use semantic search only for the narrative content where it actually fits. Two retrieval methods, picked by the system based on the question. That’s the whole architectural difference.

Wherever you want it. On your servers, in your cloud account, or on infrastructure we manage for you. We don’t store, train on, or share your data; we don’t even keep a copy after handoff unless you ask us to.

Yes. The code, the schema, the prompts, the workflow files; all of it is yours when we’re done. You can host it yourself, run it on your own infrastructure, or pay us to keep it running. We don’t lock you in.

It always is. Part of the engagement is figuring out what’s actually in your archive, what’s worth structuring, and what to leave as narrative. The pilot phase is partly about validating that the data supports the questions you want answered before committing to a full build.

Absolutely. The Platform tier is designed for that. Embeddable widget, authentication, usage analytics, tiered access. We’ve architected this for publishers and associations that want to make it part of a premium offering or sell it standalone.

Two parts. LLM API costs (which we’ll project for you during scoping based on expected query volume; typically a few hundred to a few thousand a month) and optional support retainer ($250 to $1,500 per month). If you self-host and have your own engineer, the support retainer is optional.

The pilot puts a working demo in front of your team in 4 to 6 weeks. You can show it to stakeholders, run queries against your real data, and decide whether to scale before committing to the full build. Most clients see the “oh, this actually works” moment in the first demo.

Start the conversation

Tell us what you’d want to ask your archive.

Send us a few questions your team or your audience has been asking. We’ll write back with a realistic take on whether your data can answer them and what a pilot would look like.

  • Strato responds to every inquiry personally.
  • No commitment required. No sales pitch.
  • Clear scope and pricing before any work begins.
Currently accepting Pilot projects for Q3
MC2.0 - Custom Knowledge

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