Two weeks. Use-case triage, six-month roadmap, build-vs-buy decisions, kill list. The deliverable is fifteen pages, not a hundred.
/ AI Development Company /
We build and deploy. Others stop at the deck.
Most firms in this category are dev shops in new clothes. We are two operators who run a live AI product on Shopify and have put twelve agents into production in the last ninety days.
Most AI dev shops are staffed by engineers. Ours are run by operators.
Most AI development companies
Dev shops in new clothes - engineers building artifacts, not systems
Multi-quarter discovery phases that expand to fill the calendar
Staffed by contractors - different faces on every call, no continuity
Bid hours instead of outcomes - incentive to expand not deliver
Hand you the build and a thank-you note - you own the monitoring
JAAX Labs
Two operators who run a live product - we know what breaks
Fourteen days from kickoff to first production deploy
Same two faces from kickoff to handoff - no junior layer
Fixed price, hard stopped. Refundable if it doesn't ship to production.
Ship the dashboard with the agent - we stay until the buyer reads the number
Four steps. Always the same order. We have written them down because we have caught ourselves trying to skip step one.
Step 1
Use-case triage
Which AI projects will pay off. We kill the ones that don't before you spend on them. The triage is a written assessment per project against a fixed rubric - data availability, integration cost, whether the data is clean enough to matter, who owns the number that proves it worked.
Step 2
Minimum viable AI
Ship the smallest useful thing first. The eval (automated test suite that scores model output) is the spec. We write 20–40 hand-rated examples before a single prompt. Production from week one, even if it means a feature flag and three users.
Step 3
Production hardening
MLOps (the operations layer that keeps the agent running reliably in production), monitoring, cost controls, drift detection. The dashboard ships before the agent is interesting. If the buyer cannot see the number move, the value did not happen.
Step 4
Team enablement
We leave you self-sufficient. Runbooks, evals, training, the README every junior engineer should be able to read on their first Monday. We are done when you don't need us.
/ What you actually get /
Agents, RAG systems, and the integration layer - built for your stack.
We build five categories of AI work with equal rigor. Custom agents, RAG (retrieval-augmented generation) systems, ML pipelines, workflow tools, and the integration layer that holds it all together.
Book a fit call →AI Development · Project Scope · JAAX Labs
What We Build: Five Categories
01
Custom agents in your stack
Support deflection, lead qualification, refund triage, internal ops automation. Four customer-facing, five internal-ops, three creative-assist in our last ninety days.
02
Retrieval-augmented generation
Answers grounded in your knowledge base, docs, tickets, structured data. Citation trail visible. RAG is not shipped, it is tuned.
03
Machine learning pipelines
Where the problem is tabular and the answer is a number. XGBoost, scikit-learn, sometimes a small transformer before we reach for an LLM.
04
Workflow tools and copilots
The unsexy seventy percent of real AI work in 2026. Tools that draft replies, pull history, flag churn risk. They save the hours the company actually feels.
05
Integration layer
We wire in the retry queues, cost caps, and audit logs that keep the system running in month four. Sixty percent of engineering hours. Ninety percent of why the system is still running in month four.
/ Engagements & pricing /
Four shapes. Ranges from $25k to $150k+.
We will tell you on the fit call which one you are. We publish ranges because we hate the call where you ask the price and three weeks of email-tag begin.
Fourteen days to one agent live in your stack. Eval harness (automated test suite that scores model output), dashboard, runbook included. Refundable if it doesn't ship.
Six to twelve weeks. Multi-agent systems, MLOps (the operations layer that keeps the agent running reliably in production), monitoring, integration into your CRM/ops/support stack. We stay until you don't need us.
Founders embedded with your team. Architecture decisions, code review, eval design, hiring help. Quoted by scope.
/ How we know this works - Sentinel /
We built and run Sentinel. Here's what that taught us.
Sentinel is a live Shopify analytics product. Real merchants. Real revenue. Bugs we have fixed at two in the morning. Every habit on this page - testing before building, shipping the dashboard before the agent, and refusing projects that can't be scoped to two-week chunks - was earned shipping it. The proof is not a portfolio. The proof is that you can buy it.
See Sentinel
12
agents in production
92%
renewal rate
14d
typical sprint
2
founders, no juniors
/ Who this is for /
For the team that has tried once and learned the difference between a demo and a deployed thing.
The buyers we do our best work for share three traits:
- A number they want moved - deflection rate, recovery rate, time-to-quote, cost-per-ticket
- At least one AI initiative already attempted - they know the difference between a working agent and a working demo
- A window, usually a quarter, to show something running
Series A startups whose founder is the buyer. Mid-market companies with a head of data who has just been handed AI as a portfolio. Fortune 1000 divisions that have given up on the global AI office and want to ship one thing well in their own P&L. The work is the same; the procurement is different.
"The eval is the spec. The prompt is the implementation detail. The plumbing is the moat."
From the JAAX methodology
Questions we get on every fit call.
An AI development company designs, builds, and operates production AI systems - custom agents, RAG systems, machine learning pipelines, and the integration glue that lets them survive contact with a real business. The honest version of the category builds and runs the systems it sells; the dressed-up version writes a deck and hands the build to someone else. We are the first kind. The proof is Sentinel, our live AI analytics product on Shopify.
Big Four practices sell strategy and subcontract the build. The strategy and the build then drift apart. We do both, in order, because the only way to write an AI development plan worth paying for is to have shipped enough of them to know what breaks. We have shipped twelve agents in ninety days. The full write-up is public. Read it before the fit call.
Offshore shops bid hours; we sell outcomes. Offshore shops scale by adding bodies; we cap at two and refuse work that doesn't fit. Offshore shops hand you a build and a thank-you note; we ship the dashboard with the agent and stay on the phone until the buyer can read the number themselves. The trade is real - we cost more per day. The math works because our days move the metric and theirs often don't.
Strategy sprints run $25–45k. Production proofs-of-concept run $50–150k. Full implementations start at $150k and scale with integration depth. Embedded team augmentation is a monthly retainer. We publish ranges because we hate the call where you ask the price and three weeks of email-tag begin.
Two weeks for a strategy sprint. Fourteen days from kickoff to first production deploy on a proof-of-concept. Six to twelve weeks for a full implementation. We refuse engagements that don't fit a two-week window at the unit level - if the work cannot be sliced into 14-day deliverables, we have not finished scoping it.
We standardize on Claude and reach for OpenAI, open-source, or specialized models when the eval says we should. Tooling is boring on purpose - TypeScript, Python where it earns it, Cloudflare and AWS, Postgres, the LLM SDKs directly. We do not build on whatever is trending unless it is genuinely better than what is working.
Custom agents (support, qualification, recovery, ops), retrieval-augmented generation systems on top of internal documents and structured data, machine learning pipelines with proper drift detection and retraining, and the integration layer between all of it and your CRM, helpdesk, e-commerce, or ops stack. Of the twelve agents we shipped in ninety days, four were customer-facing, five were internal-ops, and three were creative-assist.
Yes, mutual NDA before any technical conversation. We do not work for clients with conflicting active engagements in the same competitive set during a quarter - a rule we enforce on ourselves more strictly than most clients ask us to.
Send a paragraph. We'll come back the same day.
Tell us what you want shipped and the number you want moved. We'll come back with a yes, a no, or a sharper question. No discovery deck, no pitch meeting marathon.
Book a 30-min fit call