Your competitors are
shipping AI.
Ship yours in 6 weeks.
We build AI that takes real work off your team's plate, inside the systems you already run. It's built to hold up with real customers, and you own every line.
30 minutes with an engineer, not a salesperson. You leave knowing whether AI is the right answer to your problem — including an honest "it isn't" when that's the truth.
- Code in your repository from day one
- IP transferred to you in writing
- No lock-in to us or any AI provider
What we've seen break.
Specific failure modes we've diagnosed and fixed, the kind that don't show up until production. Each one links to the full write-up.
RAG pipeline returning confident wrong answers
- Why it broke
No retrieval eval layer. The vector search was returning plausible but semantically off chunks. The LLM hallucinated confidently on top of bad context.
- How we fixed it
Retrieval precision/recall benchmarks + chunk-level confidence scoring before any answer is surfaced.
LLM agent taking unintended actions in production
- Why it broke
No output schema enforcement on tool calls. The agent was given a list of actions it could take. With ambiguous prompts, it chose the wrong one — at scale, automatically.
- How we fixed it
Strict output schemas + action whitelists + human-in-the-loop gates on all write operations.
Inference costs 40× higher than projected at scale
- Why it broke
Nobody modelled token usage at volume. A feature that cost $0.002 per call looked fine in staging. At 500k calls/day it was a $30k/month surprise.
- How we fixed it
Token budget modelling in the architecture phase + cost dashboards from day one of production.
Model provider API change breaks entire product
- Why it broke
Direct coupling to one provider's API throughout the codebase. When they deprecated a parameter, every call broke simultaneously. The fix took three weeks.
- How we fixed it
Provider abstraction layer from day one. Swap models in config, not in 200 files.
Voice agent confidently answers questions it wasn't trained for
- Why it broke
No out-of-scope detection. The agent was given a knowledge base and told to answer questions. It extrapolated freely — giving confident wrong answers on topics outside its domain. Customers escalated angry, not confused.
- How we fixed it
Intent boundary classifier before the LLM layer. Unknown intents route to human, not to the model.
Vision model that aced the lab but failed on the line
- Why it broke
Training data was clean studio images. Production camera had variable lighting, motion blur, and lens distortion. Model accuracy dropped from 94% in testing to 61% in the field. Nobody had tested on real hardware.
- How we fixed it
Always train on real deployment hardware. Augment with lighting variance, motion, and sensor noise from day one.
Facing a problem that isn't listed here?
Tell us what's going wrong. If we've seen it before, you'll hear how we fixed it. If we haven't, we'll tell you that too.
Everything you need.
One engineering partner.
01AI Consulting & StrategyFind out where AI will pay off, before you spend on it.
We audit your business, identify high-leverage AI opportunities, and build a phased AI consulting roadmap grounded in ROI — not hype. Want to try the scoring yourself first? Use our free tool to prioritize AI use cases by ROI. Not sure where to start? See our AI readiness assessment.
02End-to-End AI Product DevFrom prototype to live product: assistants, agents and search over your own data.
From prototype to production. As an AI development company we design and engineer full AI products — LLM integration, AI agent development, RAG systems, generative AI development, and custom model fine-tuning — built to survive real-world scale. See it in action for e-commerce.
03AI Infrastructure & MLOpsKeep AI fast, reliable and affordable once real users arrive.
Reliability and scale are non-negotiable. Our MLOps work builds the pipelines, monitoring, CI/CD, and infra that keeps your AI system performant, observable, and cost-efficient in production.
04Voice AI SystemsPhone agents that handle real customer calls and hand off to people.
Conversational voice AI agents that handle real customer calls — understanding intent, managing dialogue flow, escalating to humans when needed. Built for production telephony, not demo environments.
05Vision AI & Edge DeploymentCameras that inspect, count and grade, right on the device.
Custom computer vision models trained on your specific domain — deployed on edge hardware, embedded systems, or cloud inference. From prototype to grading line without the latency of a cloud round-trip.
We'll tell you how we'd build it, and what it would take.
Structured for speed.
Built for depth.
Stack Audit & Architecture Review
We map your existing infrastructure — databases, APIs, data pipelines, auth systems — and identify exactly where AI slots in without breaking what works. You give us access. We give you a gap analysis.
You getStack map + integration risk doc
Technical Blueprint & Trust Spec
A precise architecture doc: system diagrams, model selection rationale, data flow maps, cost projections, and a written Trust Spec covering privacy, guardrails, and compliance — before a single line of code.
You getArchitecture doc + Trust Spec PDF
Prototype → Your Engineers Review It
A working proof of concept against your real data. Your engineers review the code, ask questions, push back. We iterate. No black boxes — your team can read everything we write.
You getReviewed, commented prototype + eval baseline
First Feature Ships to Production
Your first AI feature goes live — the one from the prototype, now production-hardened with logging and guardrails, and benchmarked against a defined quality threshold before it ships.
You getLive production feature + eval baseline
Harden, Scale & Hand Over
We broaden eval coverage, add fallback logic and CI/CD, and extend the system to the remaining scope — then hand over everything: source code, infra configs, runbooks, IP transferred in writing, and a live knowledge transfer session. You can operate this without us from day one of handoff.
You getExpanded eval dashboard + CI/CD + full codebase, runbooks & IP transfer
What we've actually delivered.
How we did it
Reduced support ticket volume by 64% after deploying an LLM-powered triage and auto-resolution layer on top of their existing Zendesk + Postgres stack. No data migration. Live in 5 weeks.
How we did it
Built a document intelligence system that extracted structured data from unstructured loan applications with 94% accuracy — replacing a 12-person manual review team for first-pass triage.
How we did it
Shipped a personalised AI recommendation engine integrated directly into their existing Django backend. Avg order value up 22% within 60 days of launch. Inference cost held under $800/month at 2M daily requests.
How we did it
Built an AI voice agent handling inbound customer queries over phone — intent classification, dynamic dialogue management, and CRM integration. Deflected 58% of calls from human agents within 30 days of go-live. Average handle time for escalated calls dropped by 34% because the agent pre-qualified context before handoff.
How we did it
Prototyped a fruit grading system using a custom vision model deployed on edge hardware — classifying produce by size, colour consistency, and surface defect with 91% accuracy at line speed. Ran entirely on-device with no cloud dependency, enabling deployment in low-connectivity environments.
Not a consultancy.
An engineering partner.
AI that your legal team
won't flag.
We know the real blocker isn't budget — it's fear. Fear of a hallucination embarrassing your company, a data leak, or a system you can't audit or control. We treat these as engineering problems with engineering solutions.
Your data never trains a third-party model. We architect data flows with strict isolation — what goes in stays in your environment.
- No data leakageTo public model providers by default
- On-premise or VPC deploymentOptions for sensitive workloads
- PII redaction pipelinesBefore any LLM interaction
- Audit logsOn every inference call, queryable at any time
Generative AI is probabilistic — but unreliable outputs are an engineering failure, not an inevitability. We build eval frameworks that catch bad outputs before users do.
- Automated eval suitesRun on every deployment
- Confidence scoring & fallback logicFor uncertain outputs
- Human-in-the-loop checkpointsFor high-stakes decisions
- Output schema enforcementStructured, validated responses
GDPR, SOC 2, HIPAA, the EU AI Act — regulatory surface area is growing fast. We design systems with compliance baked in, not bolted on after the fact.
- Data residency controlsProcessing stays in your required region
- Model cards & documentationFor regulatory audit trails
- Bias & fairness testingAs part of every eval pipeline
- Right-to-explanationArchitecture for regulated decisions
HIPAA-ready and SOC 2-ready approaches, and our vendor security overview.
We don't build systems that only work if you keep paying us — or if OpenAI changes their API. Portability and ownership are design constraints, not afterthoughts.
- Model-agnostic architectureSwap providers without rebuilding
- Repo access from day oneCode lands in your repository as it's written, not at handoff
- Full source code & IP ownershipTransferred at handoff
- Comprehensive runbooksSo your team can operate independently
- No proprietary black-box wrappersOpen, auditable stack
Your safeguards, signed off before we build.
Every Dwayo engagement includes a Trust & Safety Spec — a written document detailing exactly how your system handles privacy, failures, and compliance before a line of code is written.
See the full Trust Framework → or ask for a sample specWhy not just hire in-house?
It's the right question. Here's the honest answer.
In-house is the right answer eventually — once you know exactly what you're building and need someone to own it long-term. We get you there faster, at lower risk, and hand over everything when you're ready. If you're not yet sure what you'd hire for, the one-week Payoff Sprint works out what to build first.
Three ways
to start.
One question answered: is AI the right answer to this problem at all? A straight yes, no, or "not yet" from an engineer. No roadmap and no costings — those take real work.
Book a free call →The next question answered: what to build, in what order, at what cost. One fixed week — stack audit, ROI workshop, and a costed go/no-go roadmap that's yours whichever way it lands. Fixed fee, credited in full if you build with us.
First production AI feature in about 6 weeks, inside your existing stack. You own the code, scoped from the Sprint roadmap or from your own spec. Priced by milestone, never by the hour — you see the whole number before anything starts.
How we work ↓
Tell us what
you're trying to build.
Book 30 minutes. By the end of the call you'll know whether AI fits your problem, and whether it's worth putting a costed roadmap behind it. No pitch deck. No retainer proposal. Just a straight answer.
Or email us directly: [email protected]