Fourteen capabilities, each proven by software that is live
Every capability below names the product that proves it. Where a system is built but not yet in production, the badge says so.
AI product development
Agents, agentic systems, retrieval and the pipelines underneath them — engineered with tests, explicit tool grants and a human gate on anything irreversible.
AI agent development
A defined role with tool access and explicit permissions, built for bounded, repeatable judgement work inside a real workflow.
Agentic AI systems
Multi-step workflows and multi-agent systems on CrewAI, LangGraph and LangChain: routing, validation and orchestration held in deterministic code.
AI-powered SaaS
Products where AI does the data entry, the reading and the noticing, and a person makes every decision that matters.
Retrieval and RAG
Chunking, embedding, hybrid vector and keyword search, reranking and metadata filtering, with a relevance threshold that decides when the model should not be called at all.
Document and data pipelines
Ingestion, OCR and vision extraction, validation, typed persistence and indexing. A field the model is not sure of comes back empty and flagged, never plausible.
SaaS and software engineering
The platforms AI has to live inside: identity, permissions, billing, audit, integrations and the mobile and web surfaces people actually touch.
SaaS product engineering
Multi-tenant platforms where every customer gets their own branding, catalogue, rules and domain as configuration, not a fork.
Custom software
Systems of record for work that cannot be switched off: collection, reconciliation, ledgers and the reporting a regulator or an auditor will ask for.
API and integrations
Money transfer, AEPS, recharge, bill payment and ticketing on one login — several providers behind one contract, with ledger reporting.
Mobile applications
Native and cross-platform applications shipped to the App Store and Google Play, over a shared backend contract. Flutter and React Native, both in production.
Web applications
Web applications, APIs, backend services and content platforms — including work with no AI in it at all, where that is the right answer.
What a multi-tenant platform has to get right, and what we design in from the first sprint:
Cloud and DevOps
A system that cannot be deployed safely, observed honestly and paid for predictably is not finished, however good the model is.
Cloud & DevOps
Architecture, migration, delivery pipelines, containers, observability and cost control on 4 cloud platforms. Your accounts, in your name, from day one.
The system keeps running while you modernise it
The proposal you usually get is a rewrite. It is the wrong answer for a platform that is currently processing transactions, because the risk sits entirely with you and the value arrives only at the end.
We attach at a seam instead. New capability reads through the same API contract your existing clients use and acts only through explicit grants. Switch the layer off and the platform runs exactly as it did before.
MVP, modernisation and automation
Where to start, how to add AI to something that already works, and what to automate before writing a line of application code.
MVP development
A first working version built to test the idea against real users and real data, on a foundation that does not have to be thrown away. No-code where that is the honest answer.
Enterprise and modernisation
The platform keeps running while AI attaches at one seam: it reads through the existing API contract, proposes rather than executes, and switches off as a configuration change.
AI automation and workflows
Seven chained agents, each with a typed output schema, behind an approval gate that pauses the run and resumes on a person’s decision.
The layers we build in
Chosen per workload rather than per habit. The architecture is designed so that every one of these decisions stays reversible.
| Layer | Technologies |
|---|---|
| Backend | Python · FastAPI · Node.js · PHP · Java · .NET |
| Databases | PostgreSQL · SQLAlchemy · asyncpg · Alembic |
| Async & scheduling | Celery · Redis · Flower · APScheduler |
| Mobile | Flutter · React Native · Kotlin · iOS |
| Frontend | React · TypeScript · Vite · Next.js · Tailwind |
| Cloud platforms | AWS · Microsoft Azure · Google Cloud · DigitalOcean |
| Cloud engineering | Networking & VPC design · managed databases · object storage & CDN · secrets management · autoscaling · cost control |
| AI orchestration | CrewAI · LangGraph · LangChain · crew-manager pattern |
| Agent tooling | MCP (Model Context Protocol) · function and tool calling · typed task contracts · permissioned tool grants |
| LLM providers | OpenAI · Anthropic · Google Gemini, behind one adapter |
| Vector & retrieval | Qdrant in production. pgvector, FAISS and managed stores where the workload suits them. Hybrid search, reranking, metadata filtering, chunking strategy |
| CI/CD | GitHub Actions · GitLab CI · Bitbucket Pipelines · build, test and deploy on every merge · staged environments · rollback |
| Containers & runtime | Docker · Docker Compose · Kubernetes · Nginx · managed container services |
| Infrastructure & observability | Infrastructure as code · Terraform · centralised logging · metrics · alerting · uptime and cost monitoring |
| Quality | pytest · vitest · held-set evaluation · architecture decision records |
| Security | Access control · audit trails · ISO 27001 practice |
Tell us what has to be built
Sixty minutes on the requirement, the stack and the shortest honest path to something real in front of users.