Fix Your AI Systems in Production
Reduce LLM costs by 30%. Cut p95 latency by 60%. Stop firefighting AI in prod — and get your engineering team back to shipping features.
30-min review · Cost reduction estimate · 30-day fix plan · No sales pitch
BUILT AND OPERATED AI SYSTEMS THAT DELIVER
daily users on production AI we shipped
LLM cost reduction on a $80k/mo workload
p95 latency cut on the critical user path
Your AI works in demos.
It breaks in production.
If any of these sound familiar, your AI has graduated from a prototype to a production problem.
LLM costs are spiraling
Your monthly OpenAI / Anthropic bill is 2–5× what you forecasted. Nobody on the team can tell you which calls are driving the spend.
Hallucinations in customer flows
Customers are seeing wrong answers. You're patching prompts in production and hoping the same issue doesn't reappear next week.
p95 latency is killing the UX
Median is fine. p95 is 4–8 seconds. The users with the worst experience are the ones writing complaints — and the ones leaving.
No AI observability
When something breaks you can't trace a bad answer back to a prompt, model version, or retrieved chunk. You're debugging by reading customer screenshots.
We fix AI systems in production.
We're not a chatbot agency. We're not running prompt experiments. We engineer the cost, reliability, and latency of AI features that are already live.
WHAT WE FIX
Production-grade AI bottlenecks
Production LLM features that are too expensive. RAG pipelines that retrieve garbage. Agents that can't recover from a tool failure. Inference infra that's leaking money.
HOW WE FIX IT
Measure, then redesign
We instrument first: token-level cost attribution, prompt-level latency profiling, end-to-end tracing. Then we redesign the bottleneck — model routing, caching, retrieval, infra. We don't duct-tape.
WHY WE'RE DIFFERENT
We've operated this at scale
We've shipped and run AI features serving 30K+ daily users. We know what breaks at 100k requests/day that doesn't break in a notebook.
SERVICES
What we do
Four sharp engagements. Each one targets a specific failure mode in production AI.
AI Cost Optimization
PROBLEM
Your LLM bill is growing 4× faster than your usage.
OUTCOME
We re-architect prompt strategy, model routing, prompt-aware caching, and batching to slash spend without dropping quality.
AI Reliability Fix
PROBLEM
Hallucinations, agent loops, and tool failures are showing up in customer flows.
OUTCOME
We add guardrails, retries, structured-output validation, and fallbacks. Then we measure hallucination rate so you know it's actually fixed — not just hidden.
RAG Pipeline Fix
PROBLEM
Retrieval is returning irrelevant chunks. Your RAG looks like a search engine that gave up.
OUTCOME
We rebuild chunking, embedding strategy, retrieval ranking, and reranking — with an eval harness so retrieval quality stops regressing every time you ship.
AI Observability
PROBLEM
When a customer reports a bad answer, you can't tell which prompt, model, or retrieved chunk caused it.
OUTCOME
We instrument tracing (OpenTelemetry, Langfuse, Helicone), cost attribution per feature, and quality dashboards. Engineers ship faster because they can see what's happening.
CASE STUDY · ANONYMIZED
How a 30K-user B2B SaaS cut LLM costs 30%
and p95 latency 60%
MONTHLY LLM SPEND
−30%
$80k
Before
$56k
After
P95 LATENCY
−60%
4.2s
Before
1.7s
After
SITUATION
A B2B SaaS company serving 30,000+ daily users had shipped an LLM-powered feature that became core to their product. By month 6, the OpenAI bill had crossed $80k/month and was still growing. p95 latency on the critical user-facing call was 4.2 seconds. The team was spending sprints firefighting AI instead of shipping features.
WHAT WE DID
We instrumented end-to-end tracing first. The audit surfaced three issues: (1) every request was hitting GPT-4 even when a smaller model would have sufficed, (2) ~40% of completions were re-computations of recent identical prompts, and (3) three sequential LLM calls could run in parallel. We rebuilt model routing, added a prompt-aware cache, and parallelized tool invocations.
RESULT
LLM spend dropped from $80k → $56k/month. p95 dropped from 4.2s → 1.7s. Hallucination rate (independent of these changes) was already low and stayed inside SLA. The team got their sprint capacity back and shipped two new features in the quarter that followed.
HOW WE WORK
Four phases. No 6-month engagements that drift.
Most engagements run 6–10 weeks end-to-end. You see a measurable result by week 4.
01
Audit
We instrument your AI stack and measure what's actually happening: cost per request, p95 latency, retrieval quality, error rates. You get a written diagnostic.
02
Fix
We rewrite the bottleneck — not the whole system. Most fixes ship within 2–4 weeks of audit completion.
03
Deploy
We ship to production with shadow mode, gradual rollout, and rollback plans. No big-bang releases.
04
Monitor
Optional retainer: we own dashboards, on-call response, and ongoing tuning. Or we hand off and your team owns it. Your call.
WHO WE ARE
Built and operated AI systems at scale.
We've shipped and operated production AI features serving 30,000+ daily users. We know what breaks at 100k requests/day that doesn't break in a notebook.
Our specialty is the boring engineering work that makes AI features economical and reliable: cost attribution, model routing, prompt caching, retrieval evaluation, latency profiling, structured-output validation, on-call response.
We don't run prompt experiments. We don't build chatbots. We don't sell a platform. We engineer the AI systems that already exist in your product.
Stop firefighting AI in production.
30-min review of your stack. We'll send back a cost-reduction estimate, the top reliability gaps we found, and a 30-day fix plan.
Or email founder@themlbaba.com