Applied AI
How we work with AI
We audit your workflows for the tasks where language and vision models genuinely outperform rules — and tell you plainly where they don't. Roughly a third of the use cases we assess are better solved without AI.
A golden dataset and automated scoring before any prompt tuning. Without it you cannot tell whether a change helped, which is why so many pilots never earn sign-off for production.
Retrieval with mandatory citation, so every output traces back to a real source document. Confidence thresholds route uncertain cases to a person instead of guessing.
Per-request cost and response time tracked from the first commit, with caching, routing and model tiering designed to control them. You see a projected monthly bill before committing.
Capabilities
Adding model-backed features to existing products — summarisation, drafting, classification, extraction — without rearchitecting what already works.
2–4wks
First feature in production
Retrieval architectures over your documents and databases, tuned so answers are traceable to a source rather than confidently invented.
94%
Answer accuracy on evaluation set
Assistants connected to real systems, so they can check an order, change a booking or raise a ticket — not just restate the help centre.
68%
Conversations resolved without a human
Agents that plan, call tools, handle failure and escalate to a person when confidence drops — with every step logged and auditable.
3x
Throughput on manual review queues
Vision models for defect detection, document capture and safety monitoring, deployed to the edge where latency or connectivity demands it.
99.4%
Inspection accuracy
Combining deterministic rules with model judgement so the predictable path is fast and reliable, and only genuine exceptions reach a person.
−62%
Manual processing hours
Hybrid collaborative and semantic recommendation tuned against a commercial metric you choose, with cold-start handling built in.
+22%
Average order value
Low-latency speech interfaces for inbound and outbound calls, with transcription, intent handling and clean handoff to a human agent.
<800ms
Response latency
Semantic and hybrid search across products, documents and media, with reranking tuned to what your users are actually trying to find.
+41%
Search-to-conversion rate
Natural-language analytics, forecasting and anomaly detection over your warehouse, with the query shown so results can be verified.
−75%
Time to answer a data question
Applied AI
A dedicated programme to identify, build and productionise the AI capability with the clearest return — including the evaluation harness that keeps it reliable.
$35k–$150k
Indicative range
8–16 weeks
Typical duration
4–6 specialists
Dedicated team
Common questions
No. We deploy against enterprise API tiers with training explicitly disabled, and for sensitive workloads we can run open-weight models entirely inside your own cloud account.
Retrieval grounding with mandatory citation, confidence thresholds that escalate low-certainty cases to a human, and an evaluation suite that catches regressions before they reach production.
That is one of the most common reasons clients call us. We start by building the evaluation harness that was missing, which usually reveals within a fortnight whether the approach is viable or needs rethinking.
It depends on volume and model choice, but we instrument cost per request from the start and design caching and tiering around it. You get a projected monthly running cost before production commitment.
Start the conversation
Answer six questions and we'll map the AI opportunities in your workflow — including the ones where we'd advise a simpler solution.