Skip to content

Service Track · SVC/02 · 2-week sprint · 6-week rollout

Private Document AI & Deterministic Scoring

Put AI to work on your own data with cited answers and fair, repeatable scoring.

We engineer private retrieval pipelines (hybrid keyword + semantic search with source citations) and deterministic mathematical engines (such as Bayesian IRT psychometrics) that run on your cloud or local hardware.

Ideal Buyer Profile

Knowledge, EdTech & Compliance Teams

Typical Delivery Window

2-week sprint · 6-week rollout

The Operational Bottleneck

Why teams bring us in

Generic chatbots guess when they don't know an answer, fail to cite exact clauses or page numbers, and require uploading confidential documents or student assessments to external third-party APIs.

Diagnosed in a 2-Week Problem Discovery Sprint before full commitment.
Engineered Architecture

How we solve it in production

We pair hybrid BM25 lexical + dense vector retrieval with strict citation verification and deterministic scoring engines (like Bayesian Item Response Theory), deployed inside your private cloud VPC or local Ollama runtime.

Production Stack:
  • Python
  • TypeScript
  • Hybrid BM25 + Vector
  • Bayesian IRT
  • Ollama
  • PostgreSQL
  • Docker

What You Receive at Handover

Concrete engineering deliverables

  • Cited Document Search
  • Source Verification
  • Bayesian IRT Engines
  • Local / On-Prem LLM Runtimes

Business & Technical Impact

Verifiable operational outcomes

  • Every AI response cites the exact source document, page, and paragraph
  • Deterministic, repeatable scoring for high-stakes assessments and audits
  • Zero confidential data leaves your VPC or on-premise hardware

Buyer FAQ · SVC/02

Common questions before kicking off

How do you prevent the AI from making up answers?

We enforce retrieval-grounded generation where answers are synthesized strictly from retrieved document chunks above a relevance threshold, and every claim is linked to its source page for instant human verification.

Can this run completely offline or inside our own AWS/on-prem servers?

Yes. We deploy local-first retrieval and open-weight model runtimes (via Ollama or vLLM) so zero bytes of your data are ever sent to external model providers.