Now onboarding teams

We correct LLM errors during generation. Not after. Always Predictable.

For engineering teams shipping LLM-powered products to real users. LiveFix catches and corrects errors during generation — no retries, no extra calls. Every response returns with a trust status. You always know what to trust.

Benchmarks Request Early Access See how it works
1 callVerified output per generation
75% cheaperBudget models, premium accuracy
0 retriesCorrection during generation

LLM Evaluation & Self-Correction Platform

LiveFix provides runtime validation and self-correction for LLMs. Our system enables Inference-Optimized (Sub-Frontier) models to achieve Frontier-level reasoning and reliability.

Why Production Teams Choose LiveFix

  • 93.3% HumanEval Accuracy: Recovering logic in Sub-Frontier models at scale.
  • 85.3% TruthfulQA Score: A +12.4pp gain in hallucination prevention through instruction-override.
  • Runtime Parser Fixes: Automatic correction of multi-value ordering and bracket errors.
The Problem

Your LLM shipped wrong data. You found out when your customer did.

Your extraction pipeline reads 0.8 as 0.08. Your summarizer invents a citation that doesn't exist. Your agent calls the wrong API with hallucinated parameters. Every tool in the current stack acts after the damage is done.

Tools that watch

Langfuse, Datadog — they log the error and alert you after your user already got the wrong answer. Forensics, not prevention. You find out at 2 AM.

Tools that score

DeepEval, RAGAS — they test your prompts in staging. But production inputs are different. Errors surface on data you never tested, at scale you didn't anticipate.

Tools that block

Guardrails AI, NeMo — they catch unsafe output. But wrong, polite, well-formatted data passes every check. Safety ≠ correctness. The bad data sails right through.

Tools that re-run

2–4× your LLM cost per failure. The next attempt confidently returns a different wrong answer. Engineering teams lose hours debugging the same error patterns.

THE GAP

There is no layer in the current stack that actually fixes the output before it ships.

Until now.
The Fix

Two products. One closed loop.

Runtime correction catches errors in production. Build-time evaluation prevents them before deployment.

Runtime

LiveFix Runtime

Drop-in proxy between your app and your LLM. Every response comes back verified — with confidence scores, corrections, and trust status.

  • Corrects errors during generation — not after
  • Single LLM call — no retries, no extra cost
  • Trust status: verified · needs_review · requires_human
  • Dimension-level scoring per field
  • Adaptive — error patterns feed back automatically
Eval

LiveFix Eval

Tells you which prompt broke, why it broke, and gives you the exact fix — with expected impact on your failure rate.

  • Field-level pass/fail — not vibes-based scoring
  • AI-powered root cause analysis across chains
  • Before-and-after prompt fix suggestions
  • Business rules in plain English — no code
  • Domain-agnostic design — production-proven in healthcare
LiveFix Runtime — how it works
01
Input
Your document, prompt, and rules — zero code changes.
02
Detect
Rules extracted. Output checked field-by-field as it generates.
violations flagged
03
Fix
Corrections applied inside the same call. No extra calls. No added latency.
corrected in-flight
04
Output
Every response returns with a trust status. No silent failures.
LiveFix Eval — how it works
01
Define Rules
Write business rules in plain English. No code, no regex.
02
Run Tests
Prompt runs against real cases. Every field checked individually.
failures isolated
03
Root Cause
Pinpoints exactly which prompt line caused the failure and why.
exact cause named
04
Fix Suggestion
Exact prompt change + estimated impact on your failure rate.
Live Example

See what self-correction actually does.

Same model. Same prompt. Same document. One has LiveFix running in the middle.

Without Self-Correction
7 RULES BROKEN
HIGH internal_note
FORBIDDEN PHRASES
Do not include medication suggestions unless directly reporting a drug level.
eliquis
HIGH patient_note
FORBIDDEN WORDS
Do not include medication status, changes, or recommendations.
blood thinner
HIGH patient_note
FORBIDDEN WORDS
Do not include medication status, changes, or recommendations.
aspirin
HIGH patient_note
REFERRAL BAN
Do not promise or imply specialist referrals.
we'll schedule
LOW patient_note
FORMAT RULES
100–150 words MAX total.
75 words  ·  minimum ~100
With Self-Correction
6 FIXED
✓eliquisinternal_note
Do not include medication suggestions unless directly reporting a drug level.
✓blood thinnerpatient_note
Do not include medication status, changes, or recommendations.
✓aspirinpatient_note
Do not include medication status, changes, or recommendations.
✓prescribepatient_note
Do not include medication status, changes, or recommendations.
✓schedule a followpatient_note
Do not promise or imply specialist referrals.
✓we'll schedulepatient_note
Do not promise or imply specialist referrals.
● RULE PASS RATE — 100%
All rules passed · 6 violations corrected · single call
VERIFIED
7 → 0Violations fixed
100%Confidence
1Correction pass
9.3sTotal time
Closed Loop

Build-time + Runtime = Predictable AI.

Eval hardens prompts. Runtime catches what slips through. Error patterns feed back. The system converges.

Eval
Harden prompts
→
Deploy
Ship to prod
→
Runtime
Correct in-flight
→
Learn
Feed back
→
Result
System stabilizes
Cost Breakthrough

Same accuracy. 75% cheaper. Budget models.

Verified output means the model tier matters less. Budget models with LiveFix match premium models — at a fraction of the cost. Proven on 1,054 production documents.

ApproachModelAccuracyCost
No correctionPremium tier~95%+$4,500/month
No correctionBudget tier40–50%Unusable
With LiveFixBudget tier95.7%~$1,125/month (75% less)
real production data — LiveFix healthcare pipeline
// Before: Premium models, no correction models: Sonnet 4.5 / Opus 4.6 documents: 3,000/month cost: $4,500/month accuracy: ~95%+ // After: Budget model mix + LiveFix models: budget-tier mix documents: 3,000/month cost: ~$1,125/month accuracy: 95.7% (1,054 docs measured) failures: 45 out of 1,054 — every one flagged ───────────────────────── savings: 75% — same accuracy, budget models
Honest Breakdown

Where it works — and where it doesn't.

We'd rather be honest than overpromise.

~90% of enterprise workloads →

Structured data extraction

Invoices, contracts, claims, medical records

Classification & triage

Ticket routing, categorization, risk scoring

Schema-constrained output

JSON/XML, API responses, form filling

Business rule enforcement

Compliance, thresholds, approval workflows

Tool calls & orchestration

CRM updates, database queries, booking

Templated generation

Reports, summaries, structured notes

~10% — premium models still win

Complex multi-step reasoning

Stronger models have genuinely better reasoning chains

Nuanced multi-turn conversation

Subtle context drift may pass through on cheaper models

Open-ended creative generation

Can't upgrade writing from competent to brilliant

Very long context processing

Can't recover info the model literally can't hold

Our recommendation: Budget model + LiveFix for the 90%. Premium model + LiveFix for the 10%. Either way, every response comes back verified. You always know whether to trust the output.
Comparison

What exists today vs. what LiveFix adds.

LiveFix doesn't replace your existing tools. It adds the layer they can't provide.

CapabilityCurrent toolsLiveFix
When it actsAfter the fact or pre-productionDuring generation
Fixes the output✗ Blocks, retries, or just logs✓ Corrects in-flight
Extra LLM calls0 (passive) or 2-4x (retries)0 — same call
Catches hallucinations✗ Not during generation✓ Caught and corrected live
Catches wrong values✗ Passes safety & schema checks✓ Field-level verification
Identifies broken promptsPartially — scores, no fixes✓ Root cause + exact fix
Enables cheaper models✗✓ Closes the quality gap
Improves over time✗ Static rules✓ Daily pattern analysis
Integration

Up and running in 5 minutes. Zero architecture changes.

LiveFix is a drop-in proxy between your app and your LLM provider. Same API call you're already making. Nothing changes on your side — except every response comes back verified.

01
Configure

Provide your system prompt and validation criteria. LiveFix analyzes your use case, identifies failure modes, and generates a verification profile. Stored once. Applied on every request.

02
Integrate

One API endpoint. Drop-in proxy for your existing LLM call. Point your requests to LiveFix instead of your provider — we handle correction and forward to Anthropic, OpenAI, Google, and more. You bring your own API key.

03
Ship with confidence

Every response returns verified output plus evaluation metadata — confidence scores, correction details, and a trust status. Route verified automatically. Queue needs_review. Escalate requires_human. No silent failures.

04
Improve automatically

Dashboard shows success rates, error patterns, and smart suggestions. Error patterns feed back into daily analysis cycles. Your system gets measurably better over time — without manual prompt tuning.

Works with any LLM provider. No fine-tuning. No model changes. No new infrastructure to manage. Production-proven in healthcare — designed to be domain-agnostic.
Use Cases

Any model. Any structured task. Growing across industries.

If correctness is verifiable — there's a right answer, a required format, or a checkable rule — LiveFix closes the quality gap. Production-proven in healthcare. Expanding to legal, finance, and more.

Use caseWhat breaks todayWhat LiveFix does
Data extraction0.8 read as 0.08. Hallucinated entity names. Missing required fields.Validates every extracted value. Catches hallucinated data. Flags anomalies.
Legal document analysisInvented clauses. Unsourced claims. Misattributed provisions.Every claim must trace to the document or gets flagged explicitly.
Financial reportingTransposed numbers. Wrong calculations. Fabricated statistics.Numeric precision checks. Calculation verification. Data grounding.
Multi-turn supportContext lost after turn 3. Customer repeats themselves. Agent contradicts itself.Cross-turn consistency. Full conversation coherence validation.
Agentic tool callsWrong API called. Hallucinated parameters. Tools called out of sequence.Tool selection validation. Parameter verification. Execution order checks.
Content and RAGMade-up quotes. Non-existent citations. Embellished facts.Claims must be grounded in source material or explicitly flagged.
Credibility

Built under pressure. Not in a sandbox.

LiveFix wasn't born from a weekend hackathon or a research paper. It was built and hardened inside production systems running in regulated industries — where wrong LLM output isn't a bug, it's a liability.

Regulated production environments

Thousands of production requests in industries where wrong output isn't a UX problem — it's a compliance failure. We didn't read about LLM reliability in a blog post. We lived it.

Decades of production systems engineering

The founding team brings deep experience building enterprise software in regulated industries — healthcare technology, financial systems, and enterprise AI. Accuracy has always been non-negotiable.

Real error patterns, real consequences

The correction engine is the product of extensive iteration on real production failures. Not benchmark tuning. Every edge case in LiveFix exists because it burned us in production first.

AES-256 encrypted architecture

All keys encrypted via AWS KMS. We act as a proxy — your data passes through to your LLM provider using your API key. We don't store content beyond real-time processing and pattern detection.

"Every other tool tells you something went wrong. LiveFix corrects it before it ships — or tells you exactly what it couldn't fix."
FAQ

Questions you're probably asking.

How is this different from safety tools?
Safety tools check if output is safe — no PII, no toxicity, valid schema — then block or retry if it fails. LiveFix checks if output is correct and fixes it within the same LLM call. No retries. No extra cost. They solve different problems. Most teams use both.
Can I really use a cheaper model and get the same quality?
For structured extraction, classification, schema-constrained output, business rules, tool calls, and templated generation — yes. In our production healthcare pipeline, budget models with LiveFix match premium model accuracy at 75% lower cost. That covers ~90% of enterprise workloads. For complex reasoning, use LiveFix with a premium model — you still get verification and correction.
Which LLM providers are supported?
Anthropic, OpenAI, Google, and more. You bring your own API key. LiveFix is a proxy layer, not a replacement.
What about latency?
Correction happens within the same LLM generation — zero additional round trips. Compare that to retry-based approaches that add 2-4 full LLM calls per failure.
Does LiveFix see my data?
LiveFix acts as a proxy. Your data passes through to your LLM provider using your API key. All keys encrypted AES-256 via AWS KMS. We don't store your content beyond real-time processing.
What if LiveFix can't fix the error?
Every response includes a trust status: "verified," "needs_review," or "requires_human." No silent failures. You always know whether to trust the output.
Is this just prompt engineering?
Prompt engineering is static — write once, hope it works. LiveFix is dynamic infrastructure that adapts to your use case, learns from error patterns through daily analysis cycles, and corrects output during generation. The system gets measurably better over time — day 30 outperforms day 1.
Get started

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Early Access

Request early access.

We're onboarding teams shipping LLM-powered features to real users — who've felt the pain of inconsistent output and want predictability, not promises. No credit card. No pitch deck.

✓ No credit card required ✓ Business email only — we review every application ✓ Drop-in proxy — minimal code changes
Please use a business email address.
We'll review your application and reach out when access is ready.