AI at GooseBytes
What we build
Applied AI, focused on real systems:
- Automation and internal tools
- Search, retrieval, and knowledge systems (RAG)
- Summarization, classification, and extraction
- Decision support and data enrichment
- AI-powered product features
No demos. No bolt-ons. Built to run in production.
How we approach AI
Architecture first. Guardrails always.
- Architecture before models
- Clear success metrics and cutlines
- Observability, cost, and failure handling
- AI only where it beats simpler solutions
When simpler approaches are better, we recommend them.
From idea to production
AI follows the same discipline as our product work: decisions first, execution second.
- Discovery & constraints: Goals, data availability, latency, cost, and risk.
- Architecture & approach: Model choices, data flows, guardrails, and failure modes.
- Build & integration: Implementation inside your existing systems.
- Launch & handoff: Monitoring, testing, operational readiness.
Shipped code, not demos.
When AI is (and isn’t) a fit
AI is a good fit when it:
- Replaces manual or brittle workflows
- Improves decision quality or speed
- Integrates cleanly with existing systems
AI is not a fit when:
- A rules-based or deterministic solution is simpler
- The problem isn’t clearly defined
- Success can’t be measured operationally
We’ll tell you which is which.
How AI engagements work
AI projects follow the same fixed-scope, phased model as our other work.
- Explicit scope and assumptions
- Fixed price per phase
- Senior engineers end to end
- Re-scope when inputs change
No open-ended experimentation. No surprise invoices.
Let’s scope your AI project
One call to assess fit, risks, and next steps.
→ Book a scoping call
Response within one business day.