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Vatsal Shah

AI Product Launch

Take an AI prototype or internal pilot to a shippable v1 with paying users — architecture, build, launch, and the eval and monitoring that keep it honest after launch.

Talk to Vatsal

The gap between a working demo and a product real customers pay for is where most AI projects stall. We close it. An 8–12 week engagement that takes your pilot — internal POC, hackathon win, vendor demo — and turns it into a v1 with the eval, monitoring, billing hooks, and production hardening it needs to survive contact with real users. Code is yours from day one.

Fit for

  • Canadian companies with a working AI prototype, internal pilot, or signed-up design partner — past the idea stage, before the launch
  • Teams that have validated the use case with at least a handful of users and know what the v1 needs to do
  • Operators who want a single accountable partner for architecture, build, and launch rather than a vendor per layer

Not a fit for

  • Pre-pilot ideas without any user signal — discovery and prototyping come first; that's a different engagement
  • Teams looking only for build capacity with no input on architecture or eval strategy
  • Projects without a clear definition of who the first paying customers are and what they expect

What you walk away with

  • A v1 product deployed to production with real users — not a staging URL, not a demo
  • Production-grade architecture: queues, rate limits, retries, observability, secrets management, error handling, cost controls
  • An eval harness with at least 100 cases that runs in CI, blocks regressions, and grows with the product
  • Monitoring and alerting for latency, error rate, cost per request, quality drift, and the product-specific KPIs you care about
  • Auth, billing hooks, and entitlement plumbing so paying users can actually use it
  • A launch plan and runbook covering rollout, support intake, escalation, and the first 30 days post-launch
  • Handoff documentation so your team — or a future hire — can ship the next feature without us in the room

How the engagement runs

  1. 1

    Discovery · Weeks 1–2

    We work through the prototype, the user research, and the launch goal. Define the v1 surface, the success metrics, the eval set, and the things we are explicitly cutting from scope to ship on time.

  2. 2

    Architecture · Week 3

    Full system design — models, inference layer, data flow, integrations, auth, billing, monitoring. You see the architecture, the cost model, and the launch plan before we write production code.

  3. 3

    Build · Weeks 4–8

    Production build with weekly demos and weekly eval reports. Cut scope honestly when something will not be ready. No surprises in week eleven.

  4. 4

    Launch · Weeks 9–10

    Phased rollout — internal users, design partners, then a controlled public launch. Monitoring wired before the first real user touches it. Support intake set up so feedback flows back into the eval.

  5. 5

    Stabilization · Weeks 11–12

    First 30 days of production. Tune against real usage, close the post-launch defect list, hand off the runbook and the architecture decisions so your team owns the product end to end.

By industry

For Canadian Financial Services

AI products launched into regulated environments — member-facing copilots for credit unions, broker-facing assistants for MGAs, advisor tools for wealth platforms. Auth, audit logging, and human-review checkpoints built in. Canadian data residency options on AWS ca-central-1 and Azure Canada Central.

For Canadian Healthcare

AI products launched into clinical and operational settings — patient-facing intake assistants, provider-facing documentation helpers, ops-facing scheduling tools across multi-site clinic networks. Built around your provincial framework and integrated with the EMR or practice management system you already run on.

For Canadian Customer Operations

AI products launched for SaaS and B2B service teams — customer-facing AI features inside your app, internal copilots for support and CSM teams, partner-facing tools. Built on your existing infrastructure with billing, entitlements, and analytics wired before launch.

Selected work

Recent AI product launches.

“Vatsal is an excellent full stack developer and highly skilled project manager. He identified our business needs quickly and established a very strong framework. His incredible speed should be noted, this is a developer who doesn't waste time and hit every target date we threw at him.”
Josiah Liesemer
Josiah Liesemer
IT Specialist and Developer, Zucora Home
Read more in our engineering log →

Frequently asked

When is this the right engagement versus a smaller scope?
When you have a working pilot and the next step is paying customers. If you're earlier — exploring the idea, validating fit — the 2-Week Production Pilot is a better starting point. If you already have a v1 in production and need ongoing engineering support, Fractional AI Engineering is a better fit.
What does it cost?
Fixed fee for the full engagement, scoped on the first discovery call. Pricing depends on the size of the surface, integration complexity, and what the launch readiness gap looks like. Inference costs are passed through transparently — no markup on tokens.
What does production mean to you?
Real users, real money or real-stakes decisions, real monitoring. That means rate limits, retries, queues, observability, cost controls, auth, billing or entitlement plumbing, an eval that runs in CI, alerts that wake the right person, and a documented runbook. If any of that is missing, it isn't production.
Who does the work?
Three to four engineers from our Toronto-based team, led by Vatsal. The people who scope the engagement are the people who write the code. The full team is named on our team page — you can see and talk to them before we start.
Do we own the code?
Yes. Everything ships into your repository from commit one — the application code, the eval cases, the prompts, the infrastructure-as-code, the runbook. No vendor lock-in, no recurring license tax.
What if the launch hits a wall — bad eval results, slow adoption, surprise cost?
We share progress and risk weekly, not at the end. Bad results show up in the Friday eval report and we cut scope or change the approach in week six rather than week twelve. If the product needs to be repositioned, you hear it from us early enough to act on it.
Where does it run?
Default is your existing cloud — AWS, Azure, or GCP. For Canadian data residency, we deploy on AWS ca-central-1, Azure Canada Central, or Bedrock Canada. Inference can run through Anthropic, OpenAI, Google, or open-weights models depending on cost, latency, and residency requirements.
What happens after week 12?
Most teams keep us on a Fractional AI Engineering retainer for the next 3–6 months to ship the next set of features and respond to launch feedback. Some teams take it from there with their own engineering. Both paths are designed in from the start.