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// 05 · Service

Retrieval grounding and claim-level fact-checking

Retrieval grounded in your own data, with an eval harness that proves the answers are faithful to the source — not plausible-sounding guesses.

  • OpenAI
  • Anthropic
  • SearXNG
  • Firecrawl
  • Postgres
Start with a SprintLLM Feature Production-Readiness Checklist

// The problem

Why this is hard

A generated answer looks the same whether five sources backed it or none did. That is the whole difficulty: the failure is not an error, it is a confident sentence with nothing behind it, and the shape of the output never changes to say so. Retrieval alone does not fix it — a pipeline can fetch five pages, silently lose four, and produce prose that reads exactly as it would have with all five. What makes the difference is whether each claim is checked separately and what the system is willing to record when a check fails.

// What we build

What you get

Grounding you can repoint

Search and fetch sit behind two small interfaces, so the sources a system grounds on are an adapter choice rather than a rewrite.

Claim-level verification

The draft's check-worthy assertions are pulled out and each one is verified separately against what retrieval actually returned.

Citations that survived a check

Only the sources that supported a claim are appended as outbound links, so a reference is evidence rather than decoration.

A verdict trail and a cost ceiling

Every per-claim verdict is written to the job audit, and token spend runs against a budget gate that can stop a run.

// How it fits together

The system we build

  1. Searchadapter, not vendor
  2. Fetchdegrades per source
  3. Verifyclaim by claim
  4. Citesupported only
  5. Auditverdicts recorded
A representative shape — abstract by design; we build it in your stack and your conventions.

// Deliverables

  • Ingestion & chunking pipeline
  • Hybrid vector + keyword search
  • Grounded answer synthesis
  • Retrieval eval suite

// How we work

From prototype to production, in four moves.

01

Discovery

We map the problem, the data, and the eval that defines "done".

02

Prototype

A working slice in weeks — real model, real data, measured.

03

Production

Hardened, observable, evaluable. Shipped where users live.

04

Scale

Cost, latency and reliability tuned as load and scope grow.

// Typical engagement

What it takes to work together

  • Architecture Sprint

    $2–4kfixed · 1–2 wks

    1–2 weeks

    De-risk before you build — measured latency budget, architecture, and a costed fix list. Credited against the build.

  • Build

    from$12kfixed scope or pod

    typically 1–3 months

    Ship it end-to-end and take it to production, in your repo and conventions.

  • Run & Scale

    from$1.5k/ month

    ongoing

    Keep it working after launch — evals, latency and cost tracking, and incident response.

Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.

// Proof

Representative outcome

Healthtech — Clinical RAG

Illustrative — an anonymised, representative engagement; figures are indicative, not a verified client metric.

hallucinated citations in eval

0

on the citation eval set

source-linked answers

100%

every shipped answer

// FAQ

Common questions

Because each one is checked on its own rather than the answer being judged as a whole. Check-worthy assertions are pulled out of the draft, evidence is retrieved per claim, and each claim gets its own verdict — which is then written to the job's audit record. A claim nothing supported does not acquire a citation. What this is not is a regression harness scoring every change over time; it is a pass that runs on the work in front of it, and the audit trail is what lets you go back and disagree with it.

It stays uncited, and the verdict says why. Refusing to attach a source is the correct behaviour, and it is more useful than a plausible link that does not contain the assertion — that is the failure this whole pass exists to catch. The harder case is partial: some sources fetch and others quietly fail, so the check runs against less evidence than it appears to have. Anything that counts sources per run is worth more than anything that averages them.

Fine-tuning bakes knowledge into weights: expensive to update, and confident about facts that have since moved. Retrieval fetches at generation time, so what the model says is traceable to something that existed when it said it — and traceable is the property that matters when you have to defend a sentence later. Most production systems retrieve first and fine-tune only for format or tone.

// Related

Related services

All services

// Industries

Where teams put this to work:

15+systems shipped
6+ yrsin production
~4 wksto a first slice
99.9%uptime SLA

// Let's build

Ready to build with rag & knowledge systems?

Tell us where you are. We reply within a day with a concrete next step.