An engineering agency
for production AI.
Caffinix builds AI and automation into software that already exists — API integrations, workflow automation, and agents that call real systems rather than just answer questions — on backends engineered to stay up, in whichever stack the system already runs. Every build is handed over with a stated accuracy and error rate, measured against the client's own cases.
Client work funds the agency. Our own products run on the same stack, which is how we know what it does under load instead of guessing.
What we do
Four practices.
One engineering spine.
AI and automation in existing software
Most of our work is not a greenfield AI product. It is the integration, the workflow and the agent that has to live inside a system already carrying a business — calling real APIs, writing to real data, failing safely when something upstream does.
Agents and retrieval on your own data
Assistants and agents grounded in a client's documents, catalogue or ticket history, built so the answers can be checked. Retrieval, tool-calling and model routing, with the failure cases written down before the build starts.
Backends that hold up
Services engineered to be maintained rather than demoed — schemas, queues, idempotency, observability and cost metering. Java and Spring Boot where the domain is heavy, Python where the AI and data work lives, Node and TypeScript alongside the front end, Go where throughput decides. This is the part that is hardest to subcontract, and it is where we started.
White-label delivery for agencies
A share of our work ships under someone else's brand. The agency keeps the client relationship and the margin; we take the AI and backend scope their own team does not carry in-house.
How we work
Measured,
not demoed.
Most AI work is handed over as a demo that worked once. Ours arrives with a number attached, and the number is measured against cases the client wrote.
01
Scope against the workflow
We start at the business workflow, the data boundary and the quality bar — not the model. If AI is the wrong tool for a step, we say so before there is a quote on the table.
02
Measure before we build
An eval set written from your questions and your data, with the correct answers agreed up front. For an integration it is the failure table instead: what happens on a retry, a timeout, a partial write.
03
Build on a stack that survives
We pick the runtime to fit the problem, not the CV: Java and Spring Boot, Python, Node and TypeScript, Go, Next.js on the front. Postgres and pgvector for data, Kafka and Redis where volume needs them, Docker and Kubernetes to ship it, and our own model gateway so cost stays attributable per feature. Documented, tested, and yours at the end.
04
Hand over the number
Delivery includes the measured accuracy and error rate against your own cases, so you know how the system behaves before your users find out. Models change, so the number is worth re-measuring — that is what our retainers are for.
Proof
Software we run
ourselves.
Client work is covered by NDAs more often than not, so the portfolio you can open is our own. These are production systems, not case-study screenshots.
EvidenceVault
Multi-tenant compliance evidence platform — row-level isolation in Postgres, extract to control-match to audit packet.
ev.caffinix.com →Enki
Adaptive learning product, end to end: diagnostic, generated roadmap, then lessons produced on demand.
enki.caffinix.com →The rest of the shelf
Agent engines, gateways, internal consoles and shipped client products — the catalogue is public, including what is still in build.
See the products →
How we engage
Three ways
to start.
Fixed-scope build
A defined piece of work with a price and a date: an audit, an integration, a production agent with its eval harness.
Retainer
Continuous delivery on one product or one partner's overflow, with a standing re-measurement as models and data move.
Embedded capacity
Senior AI and backend hands inside an existing team for a fixed share of the month, under your process and your brand.
Agencies and studios subcontracting overflow have their own page: working with Caffinix white-label.
Where we work
Dhaka-based, globally engaged.
We work from Dhaka with clients in the UK, Europe, the Middle East, Australia and Asia. UTC+6 means a full working day of overlap with all of them, and contracting runs through UK and US entities where a client needs it.
Being here also keeps us close to commerce that moves fast — messaging-led selling, thin margins, and AI adoption judged on whether it pays for itself this quarter. That shapes how we scope: no pilot without a path to production, no model bill nobody can attribute.
Want to work
with us?
20-minute call. We'll tell you straight whether what you need fits what we ship.