How an AI engagement runs.
We start with one business outcome, connect the right data, design the AI workflow, and keep a human approval gate between insight and production.
- Scoped pilot with a clear success metric before kickoff
- AI systems built in your repos, with human review at every risky step
- Real handoff: docs, runbooks, and a system your team can own
Discovery, data mapping, model prompts, evaluation, approvals, launch. The engagement is a working system, not a slide deck.
The engagement loop
Each phase produces an artifact the next phase can use. That keeps the project grounded in outcomes, makes progress visible, and lets the client team inspect every decision.
- 01DiscoveryThe business problem, the metric that matters, and the data sources involved.
- 02DesignThe decision loop: prompts, checkpoints, and where a human must approve or override.
- 03BuildThe app, integrations, evaluation checks, and logging, in a repo you can own.
- 04ReviewYour team signs off on quality, risk, and business fit before anything runs unattended.
- 05LaunchA monitoring plan, handoff docs, and a support window so the system can be run confidently.
What makes an AI system safe to leave running
A demo only has to work once. A production system has to work on the day nobody is watching. These are the practices that get it there.
Evals decide releases, not opinions
Each workflow ships with a golden set: real examples with known-good answers. Prompt and model changes run against it before they merge, so “it feels better” never decides what goes live.
A person owns every risky action
AI drafts, classifies, and recommends. Where an action carries cost or reputation, it lands in an approval queue first, and a person approves, edits, rejects, or escalates it.
Measured against a holdout
Where the outcome is measurable, we hold back a control group and report the difference. A number your team can defend internally is worth more than a screenshot that impressed the room.
Uncertainty routes to a human, and every run is traced
Low confidence or an unfamiliar case goes to a person instead of a guess. Runs are traced end to end (input, context retrieved, output, and the decision that followed), so when someone asks why the system did that, there is an answer.
The pilot, scoped to your AI goal
- Written scope and acceptance criteria agreed before kickoff
- A working system on your data, not just a concept deck
- Commercial terms settled up front, with no scope drift mid-build
No lock-in
- Plain, documented code in your repos, not a black box
- Model and provider choices stay swappable, never hard-wired in
Data access & security
- Your data stays in your infrastructure
- Access only under contract and NDA, scoped to the work
- Read-only boundaries and human approval gates where AI acts
Cadence & accountability
- Named technical owner: Sachin Shinde, Lead Architect
- A weekly demo of working software, never a status slide
- Written status you can forward upward, partial US hours
After the pilot: scale it or own it
- Build-and-scale with the same named owner: monitoring, evaluation, measured uplift
- Your engineers pair with ours through the transition
- Post-launch support window agreed in the SOW
Company information
- Contracting entity: Realisier Labs Pvt Ltd
- Registered office: Hind Kush Towers, near Magarpatta Riverview City, Pune, Maharashtra 412201, India
- Contact: sachin.shinde@realisierlabs.com
Bring us the outcome you want. We’ll shape the AI engagement around it.
If you want an AI workflow that actually ships, we’ll map the data, the gates, the build, and the handoff into a clear pilot plan.