Approach
How we deliver with AI in the loop.
Our delivery model was designed around AI from the start. Senior engineers set direction and own the result, agents take on drafting, testing and documentation, and every change passes the same gates whoever wrote it.
The delivery loop
Five steps, on every change.
The loop runs at every scale, from a single pull request to a quarterly release. AI is involved at every step, and an engineer is accountable at every step.
- 01
Frame
We turn your goals, systems and constraints into context packs: architecture notes, domain glossaries and acceptance criteria that engineers and agents both work from.
- 02
Generate
Agents produce first drafts of code, tests, migrations and documentation. Engineers direct the work and make the design calls.
- 03
Verify
Automated tests, eval suites, security scans and human review gate every change. Nothing merges on a model's say-so.
- 04
Ship
Small, reversible releases through CI/CD and feature flags, into your cloud and your repositories.
- 05
Observe
Production telemetry, eval drift and cost per task feed straight back into the next cycle.
Principles
What does not change, whatever the tools.
Engineers stay accountable
Every change has a named engineer who reviewed it and owns it. AI speeds up the work; it never signs it off.
Evals before demos
For AI features we agree the test set and the pass mark before we build, and report against it every week.
Context is a deliverable
Architecture notes, domain glossaries and decision records are kept current. They make people and agents productive, and they stay with you.
Your code, your cloud, your keys
Repositories, infrastructure and model accounts belong to you from day one. We work with access you grant and can revoke.
Measure the work
We report delivery metrics such as lead time and change failure rate and, for AI features, quality and cost per task.
Engagement lifecycle
How an engagement runs.
The same four stages, whether the work is a three-week assessment or a year-long programme.
- 01
Discover
A call, then a short scoping exercise. We meet the people who own the problem and look at the systems involved.
You get: A written problem statement and a proposal with options.
- 02
Shape
We agree success measures, the team, the engagement model and the AI usage policy for the work.
You get: A signed scope, a delivery plan and access set up.
- 03
Build
Weekly releases, a weekly demo and a written status note. Risks and decisions are logged as they come up.
You get: Working software in your environment, every week.
- 04
Transfer or scale
Paired handover to your team, or the engagement grows to the next problem with the same people.
You get: Runbooks, decision records and a team that can run it.
Engagement models
Ways to work with us.
Fixed-fee for defined questions, monthly for ongoing work. Every model can change as the work does.
| Model | Term | What it is | Best for |
|---|---|---|---|
| Readiness sprint | 2 to 3 weeks, fixed fee | A defined question answered: an assessment, an architecture review or a scored use-case portfolio. | Deciding what to do next, with evidence. |
| Pilot to production | 4 to 10 weeks, fixed scope | One use case built on your data and released to real users, against success measures agreed up front. | Proving value before a larger commitment. |
| Forward-deployed pod | Monthly | A small senior team embedded in your business that owns an outcome end to end. | Problems that cross teams and systems. |
| Team extension | Monthly, per engineer | Senior engineers who join your team, work to your plan and report to your leads. | A clear roadmap that needs more hands. |
| Embedded advisory | Monthly, part-time | A consultant or fractional engineering leader inside your organization a set number of days each month. | Steering AI and platform decisions. |
Security and data
Safe to let in.
Engineers working inside your systems need clear rules. These are agreed in writing before work starts.
Confidentiality
NDAs and IP assignment in every contract, for Otorithm and for each engineer on the engagement.
Access
Least-privilege access to your systems, managed through your identity provider where possible and removed at roll-off.
AI usage
An agreed AI usage policy per engagement: which tools and models, which data they may see and how outputs are reviewed. Enterprise APIs with zero data retention by default.
Data protection
Data processing agreements, and EU Standard Contractual Clauses where needed. Processing aligned with GDPR and India's DPDP Act.
Secure delivery
Secrets in vaults, dependency and container scanning in CI, and encrypted, managed devices for everyone on the engagement.
Time zones
Working hours that overlap yours.
Our engineers work from India. Schedules are agreed per engagement so the overlap lands where your team needs it.
| Your team in | Time zone | Typical overlap |
|---|---|---|
| India | IST | Full working day |
| Middle East | GST | Full working day |
| Europe and UK | CET / GMT | Most of the working day, with engineers on an 11:30 to 20:30 IST schedule |
| North America | ET / PT | Three to four hours with the East Coast; schedules agreed per team for the West Coast |
What are we building?
Tell us about the problem. An engineer, not a sales team, will reply.