Production-grade AI systems — done right the first time.
Reliable, observable, cost-efficient. We build the AI systems your product needs — whether that's something new or hardening what you've already shipped.
30-min scoping call · We map the build · Fixed-scope 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
Is your AI actually production-grade?
Whether you have an ML team or none at all, AI that wasn't built to a production standard shows the same symptoms. If any of these sound familiar, it's time.
You shipped AI — but can't prove it works
The feature is live, but there are no evals and no way to show it's reliable. 95% of GenAI deployments see no measurable P&L impact (MIT NANDA, 2025) — usually because the proof was never built in.
Your LLM bill is climbing and nobody knows why
40–60% of token spend is typically pure waste, and cost-per-request varies up to 120× across models. Without cost attribution, you're flying blind on a line item your CFO can see.
You have an open req you can't fill
AI eval and forward-deployed engineering roles grew 729% year over year. The talent to make AI production-grade doesn't exist at the speed you need it — so the work stalls while the req sits open.
Your AI confidently makes things up
Customers are seeing wrong answers in production. One fabricated policy from a support bot became a public cofounder apology for Cursor in 2025. Trust incidents are embarrassing — and avoidable by design.
OUR BUILD STANDARDS
Six things we build into every system.
“Production-grade” isn't a slogan. These are the standards we commit to on day one of every build — each one a place teams routinely cut corners and pay for it later.
01
Evals built in from day one
You can't fix what you can't measure. We ship evals alongside the system so quality is provable, not assumed.
89% adopt observability, only ~52% run evals — quality is the #1 production barrier (LangChain, 2025).
02
Token cost engineered down
Routing, caching, and right-sizing are designed in from the start — not bolted on after the bill spikes.
40–60% of LLM spend is waste; cost varies up to 120× across models; routing/caching cuts 30–85% (RouteLLM, ICLR 2025).
03
Reliability math in the architecture
We design for compounding error before writing the first agent step, so the system survives multi-step flows.
An 8-step agent at 85% per-step accuracy succeeds just 27% of the time (Fiddler AI).
04
Observability as a first-class layer
Tracing, cost attribution, and quality dashboards are part of the build — so you can see what's happening in prod.
LLM observability spend: 15% → 50% of GenAI deployments by 2028; market $2.69B → $9.26B by 2030 (Gartner).
05
Vendor-neutral, swappable models
A portable model layer means no provider lock-in — swap across OpenAI, Anthropic, or open-source as the market shifts.
Enterprise LLM spend more than doubled to $8.4B in six months (Menlo Ventures, 2025) — the market moves fast.
06
Production-ready from day one
Full audit trails, decision logging, and documentation are built in — so the system is operable and handoff-ready.
42% of companies abandoned most AI initiatives in 2025, up from 17% — usually for lack of production rigor (S&P Global).
We build production-grade AI systems.
We're not a chatbot agency and we don't run prompt experiments. We build the reliable, observable, cost-efficient AI systems your product needs — new builds, or hardening what you've already shipped.
WHAT WE BUILD
Systems built for production
Agentic and RAG systems, LLM features, and the infrastructure around them — designed to be reliable, observable, and cost-efficient from the first commit, not retrofitted later.
HOW WE BUILD IT
To a standard, not a deadline
Every build ships with evals, cost controls, tracing, and reliability math designed in — our six build standards. Already shipped something? We instrument first, then harden the same way.
WHY TRUST US DAY ONE
We've operated this at scale
We've shipped and run AI systems serving 30K+ daily users. We know what breaks at 100k requests/day that doesn't break in a notebook — so we build it right the first time.
SERVICES
What we do
Four sharp engagements. Each is a fixed-scope build that leaves you with a system built to a production standard — not a slide deck.
Build a New AI System
PROBLEM
You need an agentic or RAG system in production, and you want it built right the first time.
OUTCOME
We design and ship the system end-to-end — evals, cost controls, tracing, and reliability math designed in from day one. Vendor-neutral, documented, and ready to operate.
Harden a Shipped System
PROBLEM
Your AI feature is live but isn't production-grade — unreliable, unobservable, or too expensive.
OUTCOME
We instrument first, then rebuild the weak layers to our six build standards: guardrails, structured-output validation, retrieval quality, cost controls — measured so you can prove it's fixed.
Engineer AI Cost Down
PROBLEM
Your LLM bill is climbing faster than usage, and nobody can tell you which calls drive it.
OUTCOME
We add cost attribution, then re-architect model routing, prompt-aware caching, and batching to cut spend without dropping quality — built to stay efficient as you scale.
Build the Eval & Observability Layer
PROBLEM
When a customer reports a bad answer, you can't trace it to a prompt, model, or retrieved chunk — and you have no evals.
OUTCOME
We build the layer your open req is for: tracing (OpenTelemetry, Langfuse, Helicone), per-feature cost attribution, eval harnesses, and quality dashboards your team can own.
CASE STUDY · HARDENED A SHIPPED SYSTEM
How we hardened a 30K-user B2B SaaS's shipped AI — 30% lower cost, 60% lower p95
8-week engagement
SITUATION
A B2B SaaS company serving 30,000+ daily users had shipped an LLM-powered feature that became core to their product — but it was never built to a production standard. By month 6, the OpenAI bill had crossed $80k/month and was still growing, p95 latency on the critical call was 4.2 seconds, and there was no cost attribution or tracing to explain either.
WHAT WE DID
We instrumented end-to-end tracing first as a first-class layer. It 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 BUILD
Four phases. No 6-month engagements that drift.
Most build sprints run 6–10 weeks end-to-end. You see a working result by week 4.
01
Scope
We define the build with you: the system to ship (or harden), success criteria, and the evals that prove it. You get a fixed-scope plan — no open-ended retainer.
02
Build
We build to our six standards — evals, cost controls, tracing, and reliability math designed in. Most sprints ship a working system within 2–4 weeks.
03
Deploy
We ship to production with shadow mode, gradual rollout, and rollback plans. No big-bang releases.
04
Operate
Optional retainer: we own dashboards, on-call response, and ongoing tuning. Or we hand off — documented and observable — 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 sell a platform. We build the production-grade AI systems your product needs — whether that's something new or hardening what you've already shipped.
CLIENT TESTIMONIAL
Working with ML Baba felt like collaborating with a senior engineer on our own team, not a vendor on the other side of an SOW. Transparent about trade-offs, pragmatic about scope, and relentless about delivering high-quality, production-ready work — an easy recommendation.
Vishal Patel
Founder, Derive LLC
Get your AI built right the first time.
Book a 30-min scoping call. We'll map what a production-grade build looks like for your system — new or already shipped — and send back a fixed-scope plan. No sales pitch.
Or email founder@themlbaba.com