What is model knowledge maintenance?

Model knowledge maintenance is a managed subscription service that keeps your private LLM deployment accurate over time. As your products change, prices update, and policies evolve, we apply targeted knowledge updates directly to the deployed model weights — no GPU retraining, no RAG infrastructure required.

What does a pre-deployment model audit cover?

A pre-deployment audit scans the model's internal knowledge across your defined topic areas and produces a report covering: facts the model knows correctly, knowledge gaps, outdated or wrong information, and any brand or competitor confusion. You receive the report plus an optional remediation patch before your model goes live.

Is static knowledge embedding a replacement for RAG?

For stable, well-defined knowledge, yes. If your company knowledge doesn't change frequently, embedding it directly into model weights eliminates retrieval infrastructure, removes latency overhead, and avoids context window costs. RAG remains the right choice for real-time or frequently changing data.

Which industries does the compliance package cover?

We offer industry-specific compliance packages for healthcare (clinical knowledge, drug interactions), finance (current regulations, compliance guidance), and legal (jurisdiction-specific knowledge, superseded statutes). Each package combines pre-deployment audit, audit trail generation, ongoing drift monitoring, and incident response under an annual enterprise SLA.

What is hallucination forensics?

When an LLM deployment causes a real incident — wrong medical advice, incorrect legal guidance, defamatory output — hallucination forensics traces exactly why the model produced a specific wrong answer. We identify the root cause (knowledge gap, polysemantic confusion, missing context), produce a report suitable for regulatory or legal review, and propose and implement a targeted fix.

How does model procurement and comparison work?

You define the knowledge domain and key topics you care about. We query multiple candidate open-source models against those topics and return a scored knowledge map with a procurement recommendation and cost/performance analysis. This replaces expensive, time-consuming general benchmarks with a domain-specific evaluation that reflects your actual use case.

LLM Knowledge Services

Private LLM deployments go stale. Knowledge gaps cause incidents. Compliance teams need audit trails. We offer a complete suite of managed services to keep your model accurate, compliant, and trustworthy — without GPU retraining.

Six Services. One Platform.

Every service in our catalog operates directly on model weights — no retraining, no retrieval infrastructure required. Updates are lightweight, instant, and fully auditable.

Model Knowledge Maintenance

Private LLM deployments go stale — new products, changed prices, updated policies surface as wrong answers. RAG adds complexity and latency. Full retraining is expensive. Our subscription service applies targeted knowledge updates directly to your deployed model weights: lightweight, instant, and audit-ready.

Submit updates as new facts, corrections, or deletions — applied directly to model weights
Instant propagation: no GPU infrastructure, no recompilation, no deployment cycle
Full audit trail of every knowledge change applied to your model
Monthly subscription based on number of models managed and update volume

Enquire About Subscription

Pre-Deployment Model Audit

Enterprises often don't know what an LLM actually knows about their domain before deploying it. Gaps and errors surface when customers complain. Our structured audit scans the model's internal knowledge across your defined topic areas and delivers a clear report before go-live.

Correct knowledge: what the model knows accurately in your domain
Gaps: topics where the model has no reliable knowledge
Wrong or outdated information: facts that will cause real-world errors
Brand and competitor confusion: misattributed facts before they reach your customers

Fee: Per-audit
Remediation Patch: Optional

Request an Audit

Regulated Industry Compliance Package

Healthcare, finance, and legal companies face strict regulatory requirements around AI accuracy and explainability. Our industry-specific compliance package combines pre-deployment audit, audit trail generation, ongoing knowledge drift monitoring, and incident response under a single enterprise SLA.

Healthcare: clinical knowledge accuracy, drug interaction verification, medical terminology
Finance: current regulations, compliance guidance, jurisdiction-specific rules
Legal: jurisdiction-specific knowledge, superseded statutes, evolving case law
Annual enterprise contract with SLA — designed for compliance officers and legal/risk teams

Discuss Your Compliance Needs

Static Knowledge Embedding

Many companies use RAG for knowledge that rarely changes — adding latency, retrieval errors, and infrastructure costs for facts that could simply be in the model itself. Our one-time embedding service places stable company knowledge directly into model weights with no retrieval overhead.

Zero retrieval infrastructure: no vector database, no embedding pipeline, no chunking strategy
No latency overhead: knowledge is in the model, not retrieved at query time
No context window cost: stable facts don't consume prompt tokens
Best for comprehensive, stable, well-defined knowledge — not real-time or frequently changing data

Assess Fit for Your Use Case

Model Procurement & Comparison

Companies evaluating which open-source model to deploy run expensive, time-consuming benchmarks that test general capability — not domain-specific knowledge. Our comparison service evaluates multiple candidate models against your actual knowledge requirements and delivers a scored recommendation.

You define the knowledge domain and key topics — we design the evaluation
Multiple candidate models queried and scored against your requirements
Scored knowledge map with procurement recommendation
Cost/performance analysis covering inference cost, model size, and licensing

Request a Comparison

LLM Hallucination Forensics and Incident Response

Hallucination Forensics & Incident Response

When an LLM deployment causes a real problem — wrong medical advice, incorrect legal guidance, defamatory output — you need to explain what happened, why, and what you did to fix it. Our forensic analysis traces the root cause, produces a report suitable for regulatory or legal review, and implements a targeted fix.

Root cause tracing: knowledge gap, polysemantic confusion, or missing context
Report suitable for regulatory or legal review
Targeted fix applied directly to model weights — not a system prompt patch
Per-incident fee with retainer option for ongoing coverage

Report an Incident

SERVICE TIERS

Find the Right Service for Your Stage

Our services map to three stages of the enterprise LLM lifecycle: evaluation, deployment, and ongoing compliance

Audit Tier

Pre-deployment audit and model procurement comparison. For companies evaluating open-source models before committing to a deployment. Per-engagement pricing.

Maintain Tier

Model knowledge maintenance subscription and static knowledge embedding. For companies with deployed models that need to stay accurate over time. Monthly subscription.

Comply Tier

Full compliance package, hallucination forensics, audit trail generation, and incident response. For regulated industries with strict accuracy and explainability requirements. Annual enterprise contract.

Talk to Us About Your LLM Deployment

Tell us about your model, your industry, and your challenge. We'll identify the right service and provide a scoping proposal within 2 business days.

AiMingle, s.r.o.
Čistovická 1729/60
163 00 Praha 6
Czech Republic, EU

+421 2 33 456 826

support@flowhunt.io

Frequently asked questions

: Model knowledge maintenance is a managed subscription service that keeps your private LLM deployment accurate over time. As your products change, prices update, and policies evolve, we apply targeted knowledge updates directly to the deployed model weights — no GPU retraining, no RAG infrastructure required.
: A pre-deployment audit scans the model's internal knowledge across your defined topic areas and produces a report covering: facts the model knows correctly, knowledge gaps, outdated or wrong information, and any brand or competitor confusion. You receive the report plus an optional remediation patch before your model goes live.
: For stable, well-defined knowledge, yes. If your company knowledge doesn't change frequently, embedding it directly into model weights eliminates retrieval infrastructure, removes latency overhead, and avoids context window costs. RAG remains the right choice for real-time or frequently changing data.
: We offer industry-specific compliance packages for healthcare (clinical knowledge, drug interactions), finance (current regulations, compliance guidance), and legal (jurisdiction-specific knowledge, superseded statutes). Each package combines pre-deployment audit, audit trail generation, ongoing drift monitoring, and incident response under an annual enterprise SLA.
: When an LLM deployment causes a real incident — wrong medical advice, incorrect legal guidance, defamatory output — hallucination forensics traces exactly why the model produced a specific wrong answer. We identify the root cause (knowledge gap, polysemantic confusion, missing context), produce a report suitable for regulatory or legal review, and propose and implement a targeted fix.
: You define the knowledge domain and key topics you care about. We query multiple candidate open-source models against those topics and return a scored knowledge map with a procurement recommendation and cost/performance analysis. This replaces expensive, time-consuming general benchmarks with a domain-specific evaluation that reflects your actual use case.

Discuss Your LLM Knowledge Challenge

Whether you're deploying a model for the first time or operating a production LLM that's drifted from the truth, we have a service to match. Tell us what you're dealing with.

Learn more

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