Limited spots available — book your project
Your competitors are shipping AI features. You're still evaluating.
We help SaaS companies integrate production-grade AI into their products — in 30 days, not 6 months. Fixed scope. Fixed price. Production-ready on day 30.
The problem
The AI gap is widening every quarter.
Your board is asking about your AI strategy. Your competitors just launched an AI-powered feature. Your enterprise prospects have "AI capabilities" as a line item in their RFPs.
Hire an ML engineer
4 months to find one, 3 months to ramp, $250k+ fully loaded. Maybe they ship something by Q3.
Assign your senior engineers
Pull them off the core product for 3 months. Hope they figure out prompt engineering, model evaluation, and production reliability on the first try.
Work with us
Production-ready AI feature, integrated into your codebase, with monitoring and documentation. 30 days.
What we do
We build three things. We build them well.
We are not a general-purpose AI agency. We don't build chatbots for marketing sites. We don't run "AI strategy workshops" that end with a PDF. We ship production code into SaaS products.
AI Feature Integration
We take your product and add intelligent capabilities — copilots, smart search, document processing, automated workflows. Scoped, built, tested, and deployed in your infrastructure within 30 days.
Typical result: 25-40% support ticket reduction, 15-30% improvement in user activation.
AI Reliability Engineering
Already have AI in production? We audit it. We find the failures your monitoring isn't catching, the costs your finance team hasn't noticed yet, and the edge cases your users have already found. Then we fix them.
Typical result: 40-70% reduction in AI API costs, 90%+ reduction in AI-related incidents.
AI-Powered Internal Tooling
We build the internal tools your ops team wishes they had — intelligent triage, automated document processing, AI-assisted workflows that replace 20 hours of weekly manual work.
Typical result: 2-3 FTEs worth of operational capacity, without the headcount.
How we work
30 days. Four phases. Zero ambiguity.
Every engagement follows our SHIP framework. You know exactly what happens, when it happens, and what you get.
Scope
Architecture review. Model selection. Integration design. We produce a written scope document with acceptance criteria. You sign off before we write a single line of code.
Harden
We build in your codebase, against your APIs, in your infrastructure. Every line of code goes through your PR process. Daily async updates. Mid-project check-in call.
Instrument
Monitoring dashboards. Alerting rules. Cost tracking. Failure playbooks. The things that separate a demo from a production system.
Pass
Recorded handoff session with your engineering team. Complete documentation. 30 days of async support included. Your team owns everything.
No retainers. No open-ended timelines. No surprise invoices.
Why us
Built by a systems engineer. Not a prompt hobbyist.
Most AI consultancies are marketing agencies that pivoted. Their team learned about LLMs from a YouTube course six months ago.
Our founder spent years building distributed systems at scale — the kind where a production incident at 2 AM means real money lost. That background changes how we build AI:
- ✓Every LLM call has a timeout, a retry, and a fallback.
- ✓Every integration has cost tracking per request.
- ✓Every feature ships with monitoring before it ships to users.
We're not an AI company that learned software engineering.
We're software engineers who specialize in AI.
Results
What CTOs say after day 30.
“We spent two months trying to build this internally. They shipped a better version in three weeks. The monitoring alone was worth the engagement.”
— CTO, Series B Developer Tools Company
“I was skeptical of the 30-day timeline. They delivered on day 27. The AI feature they built is now our most-requested capability in enterprise deals.”
— Co-founder, Series A Fintech Platform
“The difference between their work and what we'd built internally was the production hardening. Fallbacks, circuit breakers, cost controls — things we wouldn't have thought about until something broke.”
— VP Engineering, Growth-Stage SaaS
FAQ
Common questions.
How is this different from hiring a freelancer?
Freelancers build features. We build production systems. That means monitoring, fallbacks, cost controls, documentation, and a handoff that actually enables your team. We also scope the work upfront with fixed pricing — no hourly billing surprises.
What tech stack do you work with?
We integrate with your existing stack. Java, Python, Node.js, Go — we adapt. For AI infrastructure, we work with OpenAI, Anthropic, AWS Bedrock, and open-source models depending on your requirements.
What if we already have something built?
Good. Our AI Reliability Engineering service is designed exactly for this. We'll audit what you have, identify the gaps, and harden it for production.
Do you build from scratch or use frameworks?
We prefer direct SDK integration over heavy frameworks. LangChain adds complexity that most production systems don't need. We use the simplest approach that meets your requirements.
We take on 3 clients per month.
We do the work ourselves. No junior developers. No project managers as middlemen. That means limited capacity. We typically book 4-6 weeks in advance.
No pitch decks. No sales reps. You'll talk directly to the engineer who'll build your system.