Adlensa
An AI competitor ad intelligence SaaS: a single-channel prototype turned into a launch-ready, multi-tenant product in 10 weeks, with Claude explaining competitors’ messaging and creative.
Explore case: AdlensaProduct & Platform Engineering
You need an AI product in front of customers, a data platform finance can trust, or a platform your teams can keep shipping on. We design and build it, take it to production and keep it running with you.
7 days
from a validated prototype to a working MVP
Adlensa · AdTech SaaS
10 weeks
from design to a production MCP server that opens a platform to AI agents
EdgeIQ · IoT platform, USA
40+ → 1
online stores in 15+ markets reporting one governed P&L
E-commerce portfolio · Middle East
5.000+
supplier partners working on one platform across 200+ stores
Grocery retail · Saudi Arabia
Pro tip:Start with AI Product Discovery, fixed price.
A working prototype on your own data, a costed plan and acceptance criteria.
About the solution
AI-native products and the platform they run on.
We build AI-native products, data platforms, and shared agent infrastructure — with security and EU AI Act compliance built in.
Let’s talkA working prototype on your data in weeks, with the riskiest assumption tested first and a costed plan for the build.
- Entry point · Fixed price
Designed around what the model does well: data that keeps its context, clear limits on what AI may decide, and quality measured automatically from the first release.
- New products
LLM applications, RAG and multi-agent workflows, wired into your internal systems and data through MCP servers and APIs.
- In production since 2024
One place for orchestration, guardrails, knowledge access and quality checks, so every new agent starts from proven parts.
- For many teams
Warehouses and pipelines with data contracts, quality checks and one source of truth for finance, BI and AI features.
- Data as its own discipline
Monitoring, run books and incident response, with AI spend kept in check by matching each task to the right model.
- After go-live
Claude, end to end.
As an Anthropic certified partner, we take Claude from the first pilot to everyday use across your teams.
Proof on your own data
A Claude-based prototype built for one clearly defined use case, so the decision to build rests on working software.
Claude inside your product
AI features your customers use every day, designed into the product from the start.
Your systems, within Claude’s reach
MCP servers and integrations that give Claude safe, permissioned access to your internal data and tools, as we shipped for a US IoT platform.
Spend that stays predictable
Usage reviewed continuously, with the right model for each task and limits sized to real demand.
How it works
Every step is accepted before the next one starts.
Day 7Working MVP
Accepted before next stepAn end-to-end flow on a real user journey that you can click through and judge.
Weeks 2–5Core capability
Accepted before next stepThe features that make the product worth paying for, reviewed with your product owner every week.
Weeks 6–8Production foundation
Accepted before next stepCloud, authentication, workspaces and separate development, staging and production environments.
Weeks 9–10Launch readiness
Accepted before next stepPlans and billing, usage limits, onboarding, monitoring and operational safeguards.
ThenRun and improve
Runs continuouslyWe operate it and ship the next increments, or your team runs it with our run book.
Example schedule · Adlensa, from prototype to commercial launch in 10 weeks.
LandEvery source, raw
Accepted before next stepStore, advertising, product-cost and currency data landed as it arrives.
ConformTyped and normalised
Accepted before next stepUTC timestamps, daily spot FX per transaction, refunds in their original earning period.
ModelGoverned marts
Accepted before next stepOne source of truth for P&L and KPIs, with data-quality checks and schema contracts.
ReconcileVariance bridge
Runs continuouslyEach difference from the legacy spreadsheet traced to a named cause.
Sign offMateriality threshold
Accepted before next stepFinance signs off against an agreed threshold, so acceptance has a clear end.
Pattern from the portfolio data platform · from spreadsheet P&L to a governed warehouse.
DesignWhat agents may touch
Accepted before next stepService structure, interface boundaries and which platform capabilities to expose, and how.
BuildThe MCP server
Accepted before next stepTools, resources and server behaviour, developed with Claude Code and the Claude Agent SDK.
IntegrateYour platform API
Accepted before next stepAuthentication, request handling and controlled exposure of approved functions only.
HardenReady for real use
Runs continuouslyFunctional tests, error handling, configuration and documentation throughout the build.
Week 10Production release
Accepted before next stepReleased into the client’s environment with a technical hand-over, ready for more tools later.
Example schedule · EdgeIQ, production MCP server in about 10 weeks.
Why it holds
The result you see...
- A clickable product within the first week, demoed weekly after that.
- A production release at the end of every increment.
- Numbers finance can sign off without a spreadsheet.
- A product your own team can keep extending.
...and the engineering that keeps it standing.
- A written spec and definition of done before each feature.
- Rules learned from each incident, enforced on every pull request.
- Tests against real data and an engineer’s review before release.
- Architecture decisions and run books kept in your repository.
- Security testing and compliance evidence inside each increment.
What you own
It lives in your repository and your cloud from day one.
Source code and infrastructure as code.
Specs, architecture decision records and run books.
Test suites, data-quality checks and the eval harness.
Platform components and golden-path templates.
Our work
Where we have done it.
Engagement
Four ways to work with us.
Same engineers, either shape. Take a fixed piece of work, or add senior people to your own team.
Fixed price
For discovery and for each increment, each with a clean definition of done.
Dedicated team
For a continuous build, and for run and support after go-live.
Augmentation
You lead, and our senior engineers work inside your process.
Custom mix
The shape changes as the work moves from discovery to build to run.
Start with a prototype on your own data.
Tell us what you're working on. A senior expert will get back to you.
Next step