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Reduce model hallucinations and improve RAG accuracy withReinforce Engine.
Hallucinations
In production, model hallucinations are unacceptable.
To help models stick to the facts, enterprises provide models with relevant documents to answer from using a Retrieval Augmented Generation (RAG) system.
However, these systems do not guarantee correct use of the provided documents: models can hallucinate, answering users with unsupported information.
Models tuned to your use case and business documents.
Reinforce Engine allows organizations to tune specialized models that can reduce hallucinations and improve user satisfaction.
By generating synthetic data directly from your documents, Reinforce Engine eliminates the need for data collection, delivering a RAG-optimized model on day one.
Workflow
Speed to production
Tune models quickly using synthetic data generated from your document corpus.
Improve RAG accuracy
Minimize model hallucinations using an AI judge to give feedback on faithfulness.
Smaller models, smaller cost
Use models as small as 8B to outperform frontier models, while reducing cost.
Delivering faithful RAG on legal documents
Aïkan is a French LegalTech company building AI-powered software for insurance companies. Aïkan is working with Reinforce Engine to develop steerable, specialized models that are highly faithful to their RAG context.
Proprietary models struggle to follow RAG guidelines, occasionally hallucinating incorrect or unsupported information.
Using a combination of production and synthetic data, as well as feedback from expert annotators, Reinforce Engine tuned a small, specialized model to be more faithful and accurate than GPT-4o.
The tuned model cites its sources, and presents its findings in a precise format preferred by legal clerks.
Reinforce Engine tuned a Llama 3.1 8B model, reducing hallucinations by 25% over GPT-4o, and 42% over Llama 3.1 8B Instruct.
Reinforce Engine models were trained using synthetic annotations, significantly reducing production time and costs.
The proposed solution is extensible; models continuously improve over time, learning from user interactions.
Ground your AI in your own knowledge.
Bring your documents and we'll show you a model that answers from them — and cites them.
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Use CasesQuestions teams ask about rag
What does rag actually involve?
We design the workflow, build the agent and the evaluation around it, run it in shadow mode against real traffic, then hand it over with a runbook. You end up owning a running system for rag plus the evidence it works, not a proof of concept that needs rebuilding.
How long before rag is live?
Four weeks is the standard implementation: discovery in week one, environments in week two, a shadow-mode pilot in week three, handover in week four. Most teams see their first agent running against real data by week three.
Do we need an ML team to run this?
No. Most clients start with strong software engineers and no ML specialists. The engagement is built so your existing team owns the system afterwards — we train them while we build rather than handing over documentation at the end.
How do you know it is working?
Every system ships with an evaluation suite: golden datasets built from your real cases, rubric-driven scoring, and regression gates in CI. Quality becomes a number you track per release rather than an opinion, and drift pages you the way a failing test would.
What happens when the agent gets it wrong?
Low-confidence and high-stakes cases route to a human queue by design. Every failure is captured with full trace context, and those traces become new evaluation cases, so the same mistake is caught automatically next time rather than recurring.
Does this run in our environment or yours?
Yours. Deployment happens inside your cloud perimeter, against your data stores and your identity provider. We integrate with the stack you already run rather than asking you to move anything into ours.
Who owns what we build?
You do. Fine-tuned weights, datasets, evaluation suites and runbooks are yours, handed over at the end of the engagement. There is no lock-in that requires us to keep the system running.
How is this priced?
A platform subscription plus a fixed-scope implementation fee, quoted in writing before work starts. Implementation is priced by engagement rather than by the hour, so a slower week costs you nothing extra.
We tried something like this before and it failed. Why would this be different?
Most failures are not model failures — they are missing evaluation, no human fallback, and no way to tell whether a change made things better. Those are the parts we build first. If we cannot define how success is measured for rag, we say so before taking the work.
What do you need from our team?
One process owner who knows the workflow end to end, one engineer with access to the systems being integrated, and a weekly 45-minute review. That is genuinely it — no standing project committee.