How we work

AI-first, not AI bolted on.

We're built AI-first, not a traditional shop bolting AI onto old habits. That makes us faster and better value, while 23 years of enterprise experience keeps every build secure and made to last. This page says exactly what that means: what is automated, what a senior engineer reviews, and what you see each week.

Book a Discovery Sprint See the gates
The Expericia team reviewing a build together at the Kochi office
A typical build

Four stages, one team, no hand-offs.

The same engineers own the result from the first conversation to post-go-live support. Scroll through a build.

A discovery workshop: walking the team through the modules on the meeting-room screen An engineer building a feature, code on the laptop and the application on the monitor A senior architect reviewing a build with an engineer over one laptop The team gathered around one laptop at go-live Discover
WEEKS 1 TO 2

Discover

Scope, architecture and a fixed price agreed. You leave with a clickable prototype, an integration map, a risk list and a date. This is the Discovery Sprint.

FROM WEEK 3

Build, AI-assisted

Production code in weekly increments. The repetitive work is generated and checked; the judgement calls are made by people. You see a working build every week, not a status report.

CONTINUOUS

Review, senior-led

Automated gates on every change, then a senior engineer reads every pull request against architecture, security and quality standards before it merges.

GO-LIVE AND AFTER

Ship and support

A tested, scheduled cutover with a rollback plan, a hyper-care window, and the same team staying on for the roadmap.

What is automated, what is reviewed

Speed with guardrails.

Sophisticated buyers hear "AI" and think sloppy, insecure, unmaintainable. Here is the line we draw, in writing. It is the same line a good engineering manager draws between a capable junior and a senior: the junior drafts, the senior decides.

Not "vibe coding your enterprise app". Disciplined, reviewed, AI-native delivery.

AI-assisted
Scaffolding, boilerplate, tests, migrations, documentation, first drafts of code and queriesWhere the work is repeatable, the machine does the typing and a person checks it.
Senior-reviewed
Architecture, data model, security, integrations, every pull requestNothing reaches production without an experienced engineer's sign-off. Not sampled: every change.
Never delegated
Decisions about your money, your data and your usersPlatform choice, what to build and what not to, how data is handled, when to ship. People, in the room with you.
What you see
A working build every week, the pipeline results, and the same team throughoutFixed price, time and material, or a dedicated team: your choice.
From a brief to production

Every change takes the same road.

A failed gate sends the change back to its author. Only a green build that a senior has read reaches your users.

  1. BriefA ticket with the user need, acceptance criteria and the design, written with you.
  2. AI-assisted buildAn engineer builds with AI assistance: scaffolding, implementation drafts, unit tests, documentation.
  3. Automated gatesTests, static analysis, dependency and secret scanning, end-to-end checks, on every push.
  4. Senior reviewA senior engineer reads the change against architecture, security and quality standards. Every pull request.
  5. Staging and your reviewDeployed to a staging environment you can open. You see it before your users do.
  6. ProductionScheduled release, monitored, with a rollback ready.
AssistantsClaude Code and GitHub Copilot, on business tiers whose terms exclude training on our prompts.
Source and CIGitHub with GitHub Actions; Azure DevOps on Microsoft-stack programmes. Every change goes through a pull request.
Quality gatesUnit and integration tests, SonarCloud static analysis, Dependabot, secret scanning, Playwright end-to-end checks.
ReviewArchitecture, data model, security and integration review by a senior engineer, recorded on the pull request.
EnvironmentsDev, staging and production per client, in your cloud account where possible, infrastructure as code.
VisibilityYou get the repository, the pipeline results and the staging URL. Nothing happens that you cannot inspect.

Tool names are current as of October 2026; we change tools when a better one earns it, and the gates stay the same.

An Expericia engineer at his desk in the Kochi office, the sprint board behind him
Guardrails

The rules we work to, so you don't have to ask.

  • Your data stays yours. Client data lives in client-approved environments. It does not go into consumer AI tools, and it goes into business-tier tools only under terms that exclude training, and only where we have agreed it with you.
  • Your IP is yours. Code, designs, prototypes and documentation are assigned to you on payment. Generated code is licence-scanned before it merges.
  • Every change is reviewed by a senior engineer. Not sampled, not only the risky ones. Every pull request, with the review recorded.
  • Security is built in, not bolted on. OWASP practices, least-privilege access, secrets in a vault, dependency and secret scanning on every push, and a security review before go-live.
  • Decisions are documented. Platform, model and vendor choices are written down with the reasons, so the next engineer, or the next vendor, can follow them.
Quality gates

The checklist you can hold us to.

A release does not ship until every line below is true. Ask to see the evidence on any build; it is in the repository.

How we engage

Pick the model that fits your scope.

Fixed price

A clearly defined need, priced after a Discovery Sprint, delivered against agreed milestones. The price holds because the scope is specific.

Time and material

Open-ended or evolving requirements. You steer scope sprint by sprint and see output weekly, with a cap you set.

Dedicated team

An extended, white-labelled arm of your company: a stable team that learns your business, works in your tools and stays.

Every model starts the same way: a two-week Discovery Sprint, from US$5,000, credited against the build.

A worked example

A B2B commerce backbone, stage by stage.

A distributor with 28,000 SKUs, customer-specific pricing and quotes handled over email. Five engineers, five months, fixed price. The full case study has the figures; this is the shape of the work.

  1. Discovery. The catalogue and pricing rules mapped; Pimcore chosen as the single source of product truth, Magento B2B kept for the storefront; scope and price fixed.
  2. Product data model. Bulk units, packaging tiers and MOQ rules modelled in Pimcore as a normalised, central catalogue, replacing attributes scattered across Magento and spreadsheets.
  3. Sync and storefront. API-based synchronisation from Pimcore to Magento, customer-group pricing, DAM for spec sheets and compliance documents.
  4. RFQ workflow. Requests for quotation and approvals moved from inboxes into the Magento B2B storefront.
  5. Tuning and handover. MySQL optimisation and performance work, then go-live. Result: quotes out 38% faster, product updates 30% faster, product-data errors down 45%.
A senior architect and an engineer sharing a laugh over a build at the Kochi office
Questions sophisticated buyers ask

Straight answers.

Is AI-written code safe to run a business on?

Only when it is reviewed, tested and owned by people who understand it. That is the whole point of our model. AI assistants produce first drafts, scaffolding and tests; every line that reaches production has passed automated gates and a senior engineer's review, exactly as hand-written code would. The failure mode people fear, unreviewed generated code shipped by juniors, is the thing our process is designed to prevent.

Who owns the code and the IP?

You do, on payment, in full. That includes the prototype and architecture from a Discovery Sprint, whether or not you build with us.

What happens when the AI is wrong?

It is caught the same way a human mistake is caught: by tests, static analysis and review, before it merges. We treat generated code as a capable junior's first draft, never as finished work. Where a tool is confidently wrong, the senior reviewer's judgement decides, and that case is written up so the team learns from it.

Do you put our data into AI tools?

Not into consumer tools, and not without agreement. We use business tiers whose terms exclude training on customer prompts, we keep client data in client-approved environments, and we agree the data-handling rules in writing at the start of every engagement. If your policy is stricter, we work to it.

How do you price?

Three ways. Fixed price, agreed after a Discovery Sprint, for well-scoped builds. Time and material, with weekly output you can see, for evolving work. A dedicated team, as an extension of yours, for longer programmes. Discovery itself is fixed price and credited against the build.

Why is this cheaper than a traditional agency?

Two reasons. The repetitive half of software work, scaffolding, tests, migrations, documentation and first drafts, is now fast, so the same senior people deliver more. And there is no layer of account management between you and the engineers: you talk to the people building it.

See it on your own project.

Two weeks, a prototype, an architecture and a fixed price. If the answer is "don't build it", we will say so.

Book a Discovery Sprint See the work
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