01 /
RAG & grounded search
Make product and knowledge discovery useful with retrieval that combines meaning, attributes and current source data.
Output: contextual answers grounded in your data.
- Your data Documents · inventory · context
- A real user need Search · speech · a task
- Ground. Route. Evaluate. Relevant context + the right model
- Your product or workflow
Evaluate & iterate
Practical tools. Purposeful choices.
A stack shaped around the problem—not the trend.
01 / WHAT WE BUILD
Useful AI starts with
a specific problem.
Four areas where data, models and thoughtful engineering can become a useful part of your product.
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02 /
Workflow & model routing
Connect multi-step tasks and route requests by complexity. Balance model capability against latency and cost constraints.
Output: a workflow with explicit routing logic.
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03 /
Voice & speech pipelines
Bring capture, transcription, translation and speech output together. Evaluate the full experience, not just a model response.
Output: a voice pipeline designed for real use.
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04 /
Product & on-device AI
Put AI into the interfaces people already use—from web and mobile products to local inference and on-device experiences.
Output: an integrated feature, not another isolated demo.
02 / AI REFERENCE WORK
Concrete engineering.
Not abstract promises.
Selected examples of AI engineering work built for real product and workflow challenges.
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- Status
- IN PRODUCTION
- Product data
- Hybrid retrieval
- Grounded answer
Hybrid product search
pgvector similarity search meets BigQuery text search across colour, size, features and descriptions.
Embeddings, chunking and context management, with weekly source-provider re-verification to keep answers aligned with inventory.
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- Status
- IN PRODUCTION
- Request
- Complexity router
- Model tier
LLM routing & cost optimisation
Self-hosted n8n automation with LDAP-backed authentication and request-complexity routing.
Routes between open-source, mid-tier commercial and frontier models—reserving the strongest models for tasks that need them.
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- Status
- PLANNED DELIVERY Q4 2026
- VAD → ASR
- Translation
- TTS
Real-time speech translation
A 14-language pipeline connecting voice activity detection, speech recognition, translation and speech synthesis.
ONNX optimisation with CPU-only and CUDA validation, across local and cloud environments. Delivery is planned, not complete.
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- Status
- OPEN SOURCE
- System audio
- VAD + queue
- whisper.cpp
On-device meeting transcription
A standalone Windows/macOS CLI for system audio from Teams, Zoom and other sources.
Silero VAD and whisper.cpp, with separate capture and inference threads linked by a queue. Speech processing stays on device.
03 / HOW WE WORK
One workflow.
An end-to-end approach.
Start with the smallest useful path through the problem. Make the trade-offs visible, then build on what works.
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Understand the workflow
Map the user need, source data and existing systems. Define what a useful outcome should look like.
Workflow map + success criteria
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Connect data and models
Build a focused pipeline. Choose retrieval, models and routing around the actual task and constraints.
A testable end-to-end pipeline
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Evaluate before scaling
Test representative cases. Review answer quality, failure modes, latency and model cost together.
Evaluation set + trade-off review
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Integrate and iterate
Connect the pipeline to your product. Plan deployment, observability, feedback and ongoing refinement.
Product integration + next steps
04 / AI-FIRST. PRODUCT-MINDED.
More than a model.
A product partner.
No complete in-house technical team? We bring product development and technical leadership alongside AI engineering—so the feature has a home in a product people can actually use.
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Product thinking
Shape the user journey, scope and technical direction.
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Full product development
Connect AI to web, mobile, back-end and existing systems.
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Technical leadership
Make architecture decisions and plan the next build phase.
05 / THE DETAILS THAT MATTER
Designed around your constraints.
Useful AI needs more than a compelling response. These questions belong in the architecture from day one.
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Data handling
What data is needed, where it can run and what should never leave your environment.
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Evaluation & human review
Representative tests, clear failure cases and the right points for human judgement.
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Deployment & operating cost
Local or cloud inference, latency budgets, model choices and the realities of ongoing operation.
06 / SCHEDULE A DEMO
Schedule a demo.
Then decide.
Tell us what you’d like to explore. We’ll arrange a call to walk through relevant AI workflows and discuss your use case.
- Send your request
- Agree on a time
- Join your demo call