QuantAimLabs is a founder-led DevOps and AI engineering consultancy. We help CTOs and engineering leaders at growing companies take AI from demo to production — reliable, observable, and cost-controlled. For your business that means faster releases, a lower cloud bill, and AI features that don't fail in front of customers. First actionable roadmap in days, not quarters.
Numbers from our founders' work building and operating production platforms for fintech, SaaS, and security companies.
cloud spend cut on a production SaaS platform in one quarter — margin straight back to the business
of recurring infrastructure incidents eliminated — engineers shipping product instead of firefighting
microservices supported at peak — we operate at enterprise scale
a modernization program 8 months behind, delivered in 3 — five months of time-to-market recovered
“The program was eight months behind when they stepped in. Three months later it was live. Not a single consultant deck — they were in the terminal with our engineers the whole time.”
Not a technical reader? Start with our plain-English answers on cost, timeline, and risk.
Stack we ship in
Most engineering orgs have shipped an AI demo. Few have shipped one that's reliable, observable, and cheaper than the manual workflow it replaced. That's the gap we close.
No evals, no version control on prompts, no rollback. Every release is a coin flip. We install evaluation pipelines and prompt CI before you ship the next one.
Token spend triples month over month and nobody knows why. We instrument, route to the right model per task, and cache at the right layer — usually 40-70% off the bill.
Agents amplify whatever ops culture exists. We harden the platform — IaC, runbooks, observability — then layer agentic workflows on top of something stable.
Kubernetes, GitOps, IaC, CI/CD, SLOs, cost guardrails. The boring foundation that makes the exciting stuff possible.
You get: IaC repos, CI/CD pipelines, SLO dashboards, runbooks your team owns.
For the business: ship faster, with a cloud bill you can predict.
Typical: 4–8 weeks · Fixed scope
Production agents with evals, retries, observability. Tool servers, RAG pipelines, copilots, MCP integrations into your existing systems.
You get: working agents with eval suites in CI, MCP servers, cost tracing.
For the business: automation capacity without new headcount.
Typical: 6–12 weeks · Outcome-tied
Data pipelines that feed the models, DevSecOps that doesn't slow you down, and product surfaces engineered for trust.
You get: hardened pipelines, threat model, compliance-ready controls.
For the business: pass audits and unblock enterprise deals.
Typical: 4–10 weeks · Scoped per system
No email gates. No dark patterns. Just three calculators we wish existed when we started.
Score how prepared your org is to ship AI in production. Get a tier (Exploratory → Production-Ready) and a prioritized next-step list.
Score me
Estimate your monthly token, compute, and storage spend for an AI-powered system at your scale. Compare provider mixes side by side.
Estimate
Tell us your constraints — cloud, scale, data sensitivity — and get a recommended agentic + DevOps stack with rationale.
Recommend
Anonymized data from a year of production AI work. Spoiler: it's almost never the model.
Why Model Context Protocol matters more than the agent framework you pick — and the three patterns we keep seeing fail.
30-min call → 5-day deep dive. Output: written assessment, risks, and a fixed-scope plan.
Architecture, ADRs, rollout plan. Reviewed with your team. Nothing built before you've signed off.
Weekly demos, infra-as-code from day one. Your engineers pair with ours — no black boxes.
Handoff with runbooks, dashboards, eval suites. Optional 90-day retainer for second-line support.
If we're not the right fit, we'll tell you in the first 10 minutes and point you to someone who is.
QuantAimLabs