AI & Intelligent Systems
Strategy, custom LLM platforms, autonomous agents, and the MLOps backbone — AI engineered with the guardrails enterprises need and the outcomes boards expect.
See related work- One workflow, production-grade
- 8–16 wksOne workflow, production-grade
- Model interactions logged for audit
- 100%Model interactions logged for audit
- Working software in your environment
- Every 2 wksWorking software in your environment
Every enterprise now has AI ambitions; very few have AI in production doing consequential work. The gap isn't models — it's engineering: data plumbing, evaluation, guardrails, cost control, and integration into the systems where work actually happens. That engineering is our practice.
We treat AI like any other mission-critical system: discovery first, measurable success criteria, phased delivery, and a Prove phase your risk and compliance teams can interrogate. We build the version that still works in month eighteen.
What this practice owns.
Not a scope of works — the things that stay our problem for as long as the engagement runs.
The data boundary — what leaves your environment, what never can, and what is redacted in between.
Evaluation: a scored test set that says whether a change made the system better or just different.
Guardrails and the audit trail, built as delivery phases rather than bolted on before go-live.
The unit economics — token spend per transaction, modelled before build and monitored after.
What we are actually building.
The same shape under most of this practice’s work: your systems feed an ingest layer, documents land beside them, and everything resolves into one store the agents read from. The evaluation and guardrail layer is the part that decides whether any of it can go near a regulated process.
6 services in this practice
Each one is a full capability with its own page — the method, what you are handed, the stack, and who it is not for.
How this practice usually starts.
We do not publish prices — scope drives them, and a number without scope is a guess. Everything else about the shape, here.
- Starts with
- A paid discovery that ends in a scored feasibility call, not a pitch
- Time to production
- 8–16 weeks to the first workflow live
- Who you get
- An AI engineer, a platform engineer and an architect, with a data engineer where the pipeline needs one
- Commercial model
- Fixed-scope discovery, then phased build
What drives the number is on how we price.
- Opportunity scoring frameworks
- Data audits
- LLM feasibility spikes
- Governance templates
- Claude & GPT APIs
- Open-source LLMs
- Vector databases
- Embedding pipelines
- LangGraph
- Evaluation frameworks
- Agent orchestration (LangGraph)
- Claude & GPT models
- Workflow engines
- Enterprise API integration
- Observability stacks
- Python ML stack
- XGBoost & deep learning
- Feature stores
- Streaming inference
- Explainability tooling
- PyTorch vision models
- Edge inference (Jetson/ONNX)
- RTSP pipelines
- Whisper-class ASR
- Indian-language models
- Kubernetes & GPU orchestration
- vLLM & inference servers
- Prompt registries
- Observability (OpenTelemetry)
- Cost analytics
Where this practice has shipped.
AI-assisted loan origination for an NBFC
An AI-assisted origination platform that reads documents, flags risk, and routes applications — approval turnaround down 68% with tighter, not looser, controls.
68%Faster approval turnaround
AI career counselling and college matching
Students talk to an AI for career counselling and find the colleges that fit them. Built in 2023 as three connected pieces: the web experience, the mobile app and the portal behind both.
Taking a tailor's measurements with a phone camera
A platform for tailors where a customer's measurements are captured from a phone camera rather than a tape measure and a paper note.
Where AI & intelligent systems comes up most.
Questions we hear about AI & intelligent systems
Every service above runs the same five-phase method. To work out which of them your situation actually needs, discuss this practice.