Deliverable 01
A modality routing layer.
One place where the choice of vendor, model, and modality is made. Testable, cost-aware, override-friendly.
S4 · Build · Phase 2 — booking now for later this year
Multi-Modal AI Integration
Video, speech, vision, and image capabilities — orchestrated across vendors, evaluated end-to-end, cost-controlled by design.
Multi-modal work looks flashy and behaves unpredictably. The interesting engineering is in the boring parts — the field-level eval bench, the confidence-gated hand-off, the routing layer that lets you switch vendors without a rewrite. Those are the parts we’ve spent the most time on.
This service ships in Phase 2. Enquiries welcome now if you’re planning a release in the second half of the year.
When to bring us in
Signals we're the right fit.
- The user experience needs to accept voice or video and produce something structured on the other side.
- You've been building on one vendor's speech or vision stack and the accuracy is drifting on the workload that matters.
- The cost of the multi-modal path is running an order of magnitude ahead of the plan.
- You need OCR, classification, or extraction to be reliable enough that a human isn't second-guessing every output.
- A regulator is going to ask "how do you know it worked" and you'd like an answer.
How we approach it
The moves that repeat.
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Choose modalities on business signal, not on demo appeal.
A well-shaped text intake is often better than an average voice one. We start by asking whether the modality earns its keep on the task, not whether it's impressive.
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Orchestrate across vendors deliberately.
One clean abstraction. Route by task, cost, and quality — not by account manager. Fall back where the workload demands it; commit where the economics warrant it.
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Evaluate at the field level.
For extraction and classification, per-field accuracy on a real eval set. For speech, WER against your own accent and vocabulary. For vision, the specific box or the specific class you care about — not leaderboard averages.
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Human-in-the-loop where accuracy matters.
Confidence-gated hand-offs. The system knows when it's guessing; humans see what it flagged; the loop closes back into the eval set.
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Design the cost curve.
Volume, unit cost, latency, and quality on the same page. Caching, pre-processing, and modality selection tuned together — not in separate meetings.
What you get
Deliverables, in plain English.
Deliverable 02
An accuracy eval bench.
Ground-truth set drawn from your workload, per-field metrics, regression gates. Ships with the code, updated as the workload shifts.
Deliverable 03
A cost + latency dashboard.
Live view of unit economics — per query, per minute of audio, per page of document. Alerts when the curve bends the wrong way.
Engagement models
Shapes the work comes in.
BUILD
Multi-modal integration build.
Fixed scope engagement for a specific capability — voice interface, document extraction, image classification pipeline. Six to twelve weeks.
EMBED
Embedded specialist.
One of our senior engineers embedded with your platform team on a quarter-by-quarter basis. Design and build together; capability stays.
Related services
Often composed with this one.
S2 · Build
Agentic Systems Engineering
Multi-step LLM workflows with clean topology, tool surfaces, memory, evaluation, and safety — built to land in production, not stall in a demo.
S3 · Build
LLM Integration
Chat, RAG, and the wider integration stack — measured, regression-tested, and cost-aware from the first prompt.
S5 · Advise
Technical & Solutions Architecture
A senior architect embedded with your programme — end-to-end design, build-vs-buy calls, vendor evaluation, technical risk review.
Ready to talk about a multi-modal ai integration engagement?
A 30-minute discovery call. We'll get to the shape of the work and whether we're the right fit.