Service
AI that earns its place in your product
Most AI features fail the same way: a spectacular demo, then production reality — hallucinations, cost surprises, latency, and users who quietly stop trusting the output. We build AI the other way around: start from a measurable job, design the failure handling first, and let the demo be the least impressive part.
We use AI where it provides measurable utility — and we'll tell you plainly when a simpler mechanism will serve you better. That judgment is the service.
Problems we solve
- A product that needs intelligent features users can actually trust
- Knowledge scattered across documents and tools that nobody can search
- Manual reading, classification, or routing work consuming skilled people's time
- An AI prototype that impressed in a demo and stalled before production
- Pressure to 'add AI' without a clear picture of where it pays off
A good fit looks like
- A concrete workflow or feature where better language/document/vision capability changes an outcome
- Organizations that care about being right more than being flashy
- Teams willing to define what success measurably looks like
We're best suited to meaningful products, technically demanding builds, and long-term digital assets — less so to trivial one-off tasks. Unsure? Ask; we answer honestly.
Capabilities
Strategy & assessment
- AI opportunity assessment across your product or operations
- Feasibility spikes with honest go/no-go recommendations
- Build-vs-buy and model-selection guidance
Systems we build
- Knowledge assistants and retrieval-augmented search with citations
- Document intelligence: extraction, comparison, navigation
- Intelligent classification and routing
- Multimodal workflows: voice, image, and text together
- Agentic workflows — only where autonomy is justified and bounded
Production discipline
- Evaluation harnesses and golden datasets
- Observability: tracing, cost and latency budgets, drift monitoring
- Human-in-the-loop approval design
- Guardrails, fallback paths, and failure-mode design
- Privacy and security architecture, EU data residency
Typical deliverables
- A written AI opportunity map with effort/impact honesty
- Working AI features integrated into your product
- An evaluation suite your team can rerun on every change
- Operational dashboards for quality, cost, and latency
- Documentation of limits: what the system must not be trusted to do
How an engagement runs
- Opportunity assessment 1–2 weeks · where AI pays, where it doesn't
- Feasibility spike a thin working slice against real data
- Productionization evaluation, guardrails, integration, observability
- Operate & improve monitored quality, cost control, iteration
Related work from the Labs
Independent HummingByte Labs studies exploring this capability — full product reasoning, openly classified.
AI Field Report Assistant
Turn a technician's voice notes and photos into structured, reviewable service reports — before they leave the site.
- Mobile
- AI
- B2B
- Automation
Clinical Document Navigator
Find the right passage in thousands of pages of patient documents — located by AI, cited to the source, verified by a person, every time.
- AI
- B2B
- SaaS
Predictive Maintenance Console
Sensor-driven maintenance planning where models propose and planners decide — built to fight alert fatigue without missing the failure that matters.
- AI
- SaaS
- B2B
- Automation
Common questions
Which models do you work with?
The current frontier and the practical middle: hosted frontier models where capability matters, smaller or open-weight models where cost, latency, or data residency dominate, and on-device models where privacy is the product. Model choice is an engineering decision we revisit as the landscape moves — not a loyalty program.
How do you deal with hallucinations?
By design, not hope: retrieval with citations, constrained output schemas, confidence thresholds that prefer 'I don't know' over plausible invention, evaluation sets that measure it, and human approval on anything consequential. The honest answer is that hallucination is managed, not eliminated — which is why workflow design matters as much as the model.
Can our data stay in the EU — or on our infrastructure?
Yes. We design for EU residency by default and can architect for private deployment or on-device inference where the sensitivity warrants it.
Will you tell us if AI is the wrong answer?
Yes, in writing, in the assessment. Several of our own Labs studies conclude exactly that — a deterministic mechanism, honestly explained, often beats a model. You're paying for judgment, not for AI by the kilo.
Discuss a applied ai project
Describe the product or process in a few sentences — we'll come back with questions and an honest read on fit.