AI features in existing products
Summaries, smart search, drafting, classification and assistants added to your web or mobile app without rebuilding it.
Generative AI App Development · Canada
We design, build and ship LLM-powered applications end to end: product thinking, UX, backend, model integration, evaluation and deployment. We run two AI products of our own, so we know what it takes to keep one working after launch.
Why it matters
The gap between a promising prototype and a dependable product is mostly engineering: streaming UIs, structured outputs, retries and fallbacks, prompt versioning, evaluation, rate limits, cost tracking, privacy and security review. Skipping it is why so many AI pilots never make it to production.
What we deliver
Summaries, smart search, drafting, classification and assistants added to your web or mobile app without rebuilding it.
Role-specific assistants for analysts, agents, support staff and engineers, connected to the systems they already use.
Versioned prompt systems, structured JSON outputs, model routing between fast and capable models, and caching to keep latency and cost down.
Golden datasets, automated scoring, user feedback capture and regression testing so quality is measured, not guessed.
React and Next.js front ends, Python and Node back ends, Postgres and pgvector, Firebase, and cloud deployment on GCP, AWS, Azure or Vercel.
Prompt-injection defences, data-handling reviews, PII redaction, key management and tenant isolation designed in from the start.
Use cases
Kitchener · Waterloo · Cambridge · Guelph
We build generative AI products and features for Waterloo Region start-ups and established companies alike, and can work alongside your in-house developers in Kitchener, Waterloo or remotely.
Waterloo Region: Kitchener, Waterloo, Cambridge, Guelph, Elmira, St. Jacobs, New Hamburg, Baden, Ayr, Wellesley, Breslau
Nearby, in person: Stratford, Brantford, Hamilton, London, Woodstock, Milton, Toronto and the GTA
Remote: everywhere in Canada, including Ottawa, Montreal, Calgary, Edmonton, Vancouver and Halifax
Process
We map the workflow with the people who run it, find the highest-value automation points and agree measurable success criteria.
A working prototype on your real data in weeks, not months, tested with real users against the agreed criteria.
Security review, evaluation suite, monitoring, permissions and integration with your systems, ready for production.
We hand over with documentation and training, or stay on to operate, measure and extend the system.
A focused prototype on real data usually takes a few weeks. A production release depends on integrations, security review and scope. We agree acceptance criteria up front so progress is measurable.
No. Chat is one interface. Many of the most useful AI features are invisible: classification, extraction, routing, drafting and summarization inside a workflow your team already uses.
Yes. We can lead the build, pair with your team, or set up the architecture and evaluation framework and hand it over.
Yes. We help start-ups add AI features, build MVPs and set up evaluation and deployment, either leading the build or working with the in-house team.
Related
Cited answers over contracts, policies, filings, tickets and internal knowledge, including questions that span many documents.
Learn moreAgents that plan, call your tools and APIs, coordinate hand-offs and ask for approval before anything irreversible.
Learn moreMVPs, SaaS platforms, portals and mobile apps, built full-stack with AI features designed in rather than bolted on.
Learn more7 min read
Agentic AI explained for business leaders: how AI agents work, where they succeed and fail, guardrails, evaluation, approvals and examples by department.
Read the guide7 min read
Microsoft 365 Copilot and ChatGPT Enterprise vs custom AI agents: what each does well, where each falls short, a decision table and how to use both.
Read the guide7 min read
RAG vs fine-tuning vs long context for enterprise documents: when each fits, why citations and access control matter, graph RAG, and how to evaluate it.
Read the guideA 30-minute call is enough to tell whether AI is a fit, what a first project would look like and what it would take to run it in production.