Rescue

AI infrastructure audit & cleanup

A previous developer (or an over-eager build) shipped something that works in the demo and falls over in production — burning tokens, leaking data, or failing silently. We audit the system, find the root cause, and deliver a clear, developer-ready roadmap. Then we do the refactor, if you want it.

What you get

  • Full audit: latency, cost, security, and failure modes
  • Root-cause diagnosis with a prioritized fix list
  • Token-cost and reliability overhaul
  • Developer-ready roadmap — and the refactor on request
Our Process

How we deliver.

A structured engagement from discovery to deployment — no surprises, no scope fog.

01

Codebase & architecture review

We read the code, trace the request lifecycle, map every external call, and document the current architecture — including the parts nobody wrote documentation for.

02

Cost & performance profiling

We instrument token usage per feature, measure latency percentiles, and identify the top cost drivers and performance bottlenecks with real production data.

03

Failure mode analysis

We catalog every way the system can fail — silent errors, unhandled exceptions, timeout cascades, and data corruption risks — and assess the blast radius of each.

04

Security & data exposure scan

We check for leaked API keys, unencrypted PII in logs, prompt injection vectors, and overly permissive access controls.

05

Prioritized roadmap delivery

We deliver a developer-ready document: ranked fixes, estimated effort, expected impact, and clear implementation guidance. Then we execute the refactor if you want us to.

Common Challenges

Problems we solve.

Problem

Token costs are 5–10x what they should be

Solution

We profile every LLM call, eliminate redundant re-processing, implement caching for repeat queries, right-size context windows, and often find 40–70% cost reductions.

Problem

The AI fails silently in production

Solution

We add structured error handling, request tracing, and real-time alerting so failures are caught, logged, and surfaced — not buried in a log file nobody checks.

Problem

Response times are unpredictable

Solution

We identify bottlenecks — oversized prompts, synchronous chaining, unoptimized retrieval — and implement streaming, parallel execution, and latency budgets.

Problem

The previous developer left and nobody understands the code

Solution

Our audit includes a complete architecture document: what each component does, how data flows, where the fragile parts are, and what to fix first. You'll finally understand your own system.

Why Lesscode

What sets us apart.

  • We've rescued AI systems built on every major provider — OpenAI, Anthropic, open-source, and custom fine-tunes
  • Our audits have saved clients a combined $200K+ in annualized token costs
  • We don't just diagnose — we deliver working refactored code, not just a PDF
  • We own and operate our own AI products, so we know what production AI actually looks like
  • Fixed-price audit — you know the cost before we start, with no surprise hours
FAQ

Common questions.

Q.How long does an audit take?

A standard audit takes 1–2 weeks. We deliver a comprehensive report with ranked findings, cost impact estimates, and a prioritized roadmap — typically 15–30 pages of actionable detail.

Q.Do you also fix what you find?

Yes. The audit is phase one. If you want us to execute the fixes, we scope a refactor engagement based on the roadmap we delivered. Many clients do both.

Q.What if the system needs a complete rebuild?

We'll tell you honestly. If the architecture is fundamentally broken, we'll recommend a rebuild and scope it. If it's salvageable, we'll fix it incrementally.

Q.Can you audit a system built by another team?

Absolutely — that's the most common scenario. We need codebase access, production logs, and a 30-minute walkthrough with whoever knows the system best.

Selected Portfolio

What we've built.

Client Testimonials

What our partners say.

Our AI token costs were out of control and queries were failing silently in production. Lesscode did an audit, diagnosed three critical bottlenecks, and completed the refactor. Costs dropped by 63% and response times are now sub-second.

Julian S.
Co-Founder, HyperScale

Building something ambitious, or fixing something that's gone sideways?

Tell us where you are and where you're trying to get to. We'll tell you honestly whether — and how — we can help.