LocusPhiFrom process mapping to measurable improvement.
We don't start by asking which AI model to use. We start by understanding the business problem.
Discover
Understand the business problem, current process and objectives before discussing which AI capability applies.
Map
Document how work actually moves today — where a person reads, decides and acts, and on what information.
Design
Design what the AI needs to understand, reason over and decide — and exactly where a person stays involved.
Build
Build the AI's reasoning, extraction and decision logic around your business rules.
Integrate
Connect the systems the AI needs to read from and act on — no forced migration to a new platform.
Test
Validate the AI's understanding and decisions against real scenarios, including edge cases and failure modes.
Deploy
Roll out in a controlled way, with monitoring on the AI's decisions in place from day one.
Measure
Track the outcomes that matter — turnaround time, decision accuracy, volume handled.
Improve
Refine rules, thresholds and scope as your process, data and needs evolve.
Engagement models
Three ways to start, depending on where you are.
Discover
Who it's for. Organisations exploring where AI can help.
A structured review of your current processes and where AI understanding, reasoning and action would create the most value.
You receive: A prioritised view of candidate solutions, with an honest read on effort and expected value.
Start with a discovery conversationBuild
Who it's for. Organisations ready to implement a defined AI solution — one of the above, or something custom.
We design, build and integrate an AI solution around your systems, data and business rules.
You receive: A working, deployed solution for the process you selected, with your team trained to use it.
Scope a buildScale
Who it's for. Organisations expanding AI across departments and systems.
We extend proven AI solutions to additional teams, systems and processes, with governance built in.
You receive: A connected layer of AI solutions across the workflows that matter most to your organisation.
Talk about scalingWhere time is typically recovered
Illustrative example — actual figures depend on your process and volumes.
What we build with
We combine modern AI frameworks such as LangChain, LangGraph, LlamaIndex, PydanticAI and agent frameworks with FastAPI, vector databases, guardrails and evaluation tooling, alongside enterprise platforms including Appian, Camunda, Power Apps, Power Automate, n8n and Zapier to build production-ready AI solutions.
AI frameworks & agents
Engineering & AI infrastructure
Governance & guardrails
Enterprise platforms
Automation platforms
The stack is selected around the workflow, existing systems, data requirements and operating model — combining custom engineering with enterprise platforms where that creates the cleanest production path.
How we govern it in production
Every AI workflow we build is engineered, tested and monitored the way production software is — not shipped as a one-off prompt and left alone.
Context engineering
We define exactly what information the AI sees for each decision — the right records, history and policy — so it reasons over complete, relevant context instead of a generic prompt guessing at what matters.
Structured orchestration
Multi-step reasoning and tool use is built as an explicit, inspectable sequence of steps (commonly with LangGraph or a comparable framework) — not a single black-box call — so every decision point can be traced, tested and changed independently.
Evaluations before deployment
Before a workflow goes live, we test it against real and edge-case scenarios and track its accuracy against a defined benchmark, not just a working demo.
Observability & tracing
Every run is traced end to end — what the AI read, how it reasoned, what it decided and why (typically with LangSmith or an equivalent) — so an unexpected outcome can be diagnosed, not just re-run and hoped away.
Guardrails & policy enforcement
Business rules, approval thresholds and output constraints are enforced outside the model, as a hard boundary the AI operates inside rather than one it could reason its way around.
Versioning & rollback
Prompts, rules and workflow logic are versioned like code, so a change can be reviewed, tested and rolled back if it doesn't perform as expected.