02 · Applied AI & Data
AI that answers to evidence, not enthusiasm.
When teams call us
You might be here because…
- 01A prototype impressed in a demo, and now it has to be reliable, affordable and safe.
- 02Your team spends hours searching documents, recordings or tickets for answers that already exist.
- 03You want an assistant or agent inside your product, with permissions that match your users.
- 04You need to know whether AI is actually the right tool for the problem.
- 05Your data is spread across systems and nobody trusts the numbers.
01What we build
Applied AI & Data, in practice.
01
LLM features and assistants
Summaries, drafting, classification and question answering built into your product, with prompts, models and fallbacks chosen for your cost and latency budget.
02
Agents and automation
Agents that take actions across your tools, with least-privilege access, explicit approval steps for anything consequential and a full log of what they did and why.
03
Retrieval and grounded answers
Hybrid semantic and keyword retrieval over documents, tickets, recordings and databases, with answers that cite their sources and decline when the evidence is thin.
04
Evaluation and monitoring
Evaluation sets built from your real cases, regression checks on every prompt or model change, and production monitoring for quality, drift, cost and latency.
05
Document, speech and vision pipelines
Turning PDFs, scans, audio and video into structured, searchable data, with the original always one click away.
06
MCP servers and tool integrations
Model Context Protocol servers and tool APIs that let AI assistants use your internal systems safely, with authentication, scoping and audit built in.
07
Data engineering and analytics
Pipelines, warehouses and models that give teams one set of numbers, with lineage you can trace and dashboards people actually open.
02How we hold the line
The standards that come with it.
- Measured before it ships
- No AI feature launches without an evaluation set that reflects real use, an agreed quality bar and known failure modes written down.
- Grounded where it matters
- When an answer must be verifiable, it is built from retrieved evidence and shows its sources. When the evidence isn’t there, the system says so.
- Humans on consequential decisions
- Actions with financial, legal or personal impact go through human review with clear escalation. Agents get the least access that does the job.
- Budgets, not surprises
- Cost per task and latency are designed targets, tracked in production, with caching and model routing to keep them there.
- Your data stays yours
- Client data is used for the engagement only. We prefer providers and settings that don’t train on inputs, and minimize and redact personal data. See Responsible AI.
03Across the loop
How this practice shows up at every stage.
01 · 000°
Design
Frame the task, collect real examples and decide whether AI is the right tool at all.
02 · 060°
Engineer
Prompts, retrieval, tools and interfaces built against the evaluation set.
03 · 120°
Integrate
Connected to your data sources and tools with scoped, audited access.
04 · 180°
Secure
Prompt-injection defences, output validation and permission checks on every action.
05 · 240°
Scale
Caching, batching and model routing that keep cost and latency inside budget.
06 · 300°
Maintain
Monitoring for drift and regressions, and re-evaluation whenever a model changes.
04Tools we reach for
Chosen for your constraints, not our habits. These are common starting points, not a catalogue.
- Models
- Hosted frontier modelsOpen-weight models where data or cost requiresSpeech, OCR and vision services
- Retrieval
- Vector and hybrid searchPostgreSQL with pgvectorRerankers
- Data
- PythonSQLStream and batch pipelinesWarehouses and lakehouses
05How engagements run
How engagements run
AI feasibility sprint
Two to three weeks to test an AI idea against your real data and give you a go/no-go with evidence.
AI feature build
Design, build and launch an AI capability inside your product, with evaluation and monitoring in place.
Data foundations
Pipelines and models that make your data reliable enough for analytics and AI.
See how an engagement runs, from the first call to handover.
Questions
What people ask first.
Whichever fits the task, data constraints and budget. We evaluate candidates against your cases, and design the system so the model can be swapped without rewriting the product.
Not with our involvement. We choose providers and settings that don’t train on inputs, and we never send client data to a model without the client’s agreement.
Least-privilege tool access, validation on every action, human approval for consequential steps, rate limits and a complete audit log. Agents act inside boundaries you can read.
Yes, and sometimes that is the most useful thing we deliver. A rules engine, a better search index or a clearer workflow is often cheaper and more reliable.
Start a conversation
Which answer does your team spend hours looking for?
A few lines are enough. We reply in writing, with questions rather than a sales deck.