CUSTOM AI SYSTEMS · RAG · REASONING · AGENTS

AI systems that show their evidence.

We design and ship custom retrieval, reasoning and agent systems for research institutions and the organizations that serve them — able to run entirely on your own infrastructure, and cited every time.

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A typical Evenflow pipeline: every stage traced, every answer tied to its sources.

01 — SERVICES

Built for questions where a wrong answer costs something.

RAG

Advanced retrieval

Ingestion of PDFs, slides, web pages and recordings; hybrid search with reranking; citations that resolve to the exact page, slide or timestamp.

REASONING

Reasoning engines

Multi-stage pipelines that triage a question, decide what evidence it needs, combine documents with live data and write for the reader in front of them.

AGENTS

Agentic workflows

Agents that process, review and route work end to end — with people approving the steps that matter, and a full audit trail of what ran.

EVALUATION

Measurement & operations

Evaluation sets, tracing and runbooks, so every change is measured before it ships and your team can run the system after we hand it over.

02 — SELECTED WORK · 2026

Two knowledge systems for the Yale Center for Ecosystems + Architecture.

Yale CEA works on how the built environment can support biodiversity, resilience, clean energy and water. This year we built the AI behind two of its platforms.

01 — IDRO · DROUGHT RESILIENCE

IDRO AI-Ki: a knowledge agent for the International Drought Resilience Observatory

A self-hosted RAG and reasoning engine that answers questions about drought resilience — the DRI framework, a country's indicator scores and the research behind them — for policymakers, scientists and the public.

Each question runs through triage, intent classification, retrieval, reranking and synthesis. Documents are scoped by country, and jurisdictions that require it keep their data — and their model traffic — on a physically separate instance. A science-policy tool can't guess, so every answer cites its evidence.

RAG pgvector BGE-M3 + reranker data residency Arize Phoenix tracing
Visit dev.idro.global →
PIPELINE
5 stages, traced
LANGUAGES
6, incl. Arabic & Mongolian
AUDIENCES
4 reader profiles
RESIDENCY
Per-country isolation

02 — SIGNAL ARCHIVE · SYMPOSIUM KNOWLEDGE

SympoKnowledge: turning a symposium into citable evidence

An evidence-first platform for Yale CEA's symposium. Organizers deposit papers, slides and session recordings; the platform turns them into reviewed fragments that can answer questions — and every answer points back to the page, slide or moment in a recording that supports it.

Recordings are transcribed and split by voice, but a speaker is only named after a person has listened. Nothing is retrievable until a reviewer approves it, and release to the public is a second, separate decision — enforced in the database, not just the UI.

Docling faster-whisper pyannote diarization human-in-the-loop review FastAPI
Visit app.yalecea-symposium.org →
CITATIONS
Page, slide or 00:19:29
APPROVALS
Two, never one
SOURCES
PDF, slides, audio
EVALUATION
48-question test set

03 — PRIVACY & DATA RESIDENCY

Your data stays where it must — by design, not by policy.

Every system we build can run entirely on your own infrastructure — language models included — so no document, question or answer has to reach an external API. Privacy and residency decide where each component runs and who can see what; here is how that played out on both projects.

IDRO AI-KI

  • Residency per country. Each country's documents are mapped to a database instance; jurisdictions that require it sit on a physically separate one.
  • Model traffic follows the data. Questions and retrieved text from an isolated jurisdiction go only to the model endpoint approved for it — and the system refuses to run rather than fall back to a shared one.
  • Self-hosted end to end. Embeddings and reranking run in local containers, and the language models can too, so documents and questions never have to leave your servers.
  • Least exposure. Users see their own country plus what is explicitly shared; uploader identities and internal errors never reach other users.

SIGNAL ARCHIVE

  • Recordings never leave the box. Transcription and speaker separation run locally, on a network with no route to the internet.
  • Local models, by configuration. Parsing, transcription and answer generation can all run on the same server, so nothing leaves it. Where a client opts for a hosted model, we use only providers that neither store nor train on the data.
  • Nobody is named by a machine. A speaker is identified only after a person has listened; until then, voices stay anonymous.
  • Release is enforced in the database. Unapproved material can't be retrieved at all, and going public is a second, separate approval.

04 — HOW WE WORK

Four rules we build by.

  1. 1

    Evidence first

    An answer without a source is a defect. Our systems cite, quote verbatim and refuse to guess.

  2. 2

    People approve what matters

    Automation does the heavy lifting; reviewers make the calls that carry risk, and every step is logged.

  3. 3

    Measured, not guessed

    Every change is tested against an evaluation set and traced stage by stage before it ships.

  4. 4

    Built to hand over

    Documentation, runbooks and backups come with the system, so your team owns it from day one.

05 — CONTACT

Tell us the question your documents should answer.

We take on a small number of projects at a time. Send a few lines about your data and your users, and we'll reply within two working days.

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