AI SaaS development where the model is accountable to your data.
Retrieval, agents and language-model features built on top of an auditable operational record — with the boundaries, costs and failure behaviour written down before launch.
AI features that can show their work.
Most AI SaaS applications fail review for the same reason: nobody can explain where an answer came from or what it cost. We treat model output like any other operational write — attributable, reproducible and bounded.
That means retrieval over your own records rather than a general model guessing, explicit tool boundaries for agents, and a logged trace for every generated result so support and compliance can reconstruct it later.
Cost and latency are specified up front, with fallbacks defined for the day a provider is slow or unavailable.
- No card, no trial clock
- Engineer replies same business day
- SOC 2 Type II · PIPEDA
- Your data stays in your region
Who this is for
- Teams adding AI capability to an existing SaaS product
- Companies automating document- or ticket-heavy operations
- Founders building an AI-first SaaS MVP
- Organisations that need AI output to be auditable, not just impressive
The failures this work is meant to end.
Answers with no provenance
A confident wrong answer is worse than no feature. Retrieval grounds output in your records and cites what it used.
Unbounded agents
An agent with open-ended access is an incident waiting to happen. Tools, scopes and write permissions are declared explicitly.
Costs that arrive as a surprise
Token spend scales with usage, not with your plan. Budgets, caching and per-tenant limits are part of the build.
Engineering scope, stated plainly.
LLM integration
Language-model features wired into real workflows: drafting, classification, extraction and summarisation where they remove measurable manual work.
- Provider-agnostic integration with defined fallbacks
- Prompt and output versioning, so behaviour changes are traceable
- Per-tenant rate and cost limits
Retrieval and RAG solutions
Answers grounded in your documents and operational data, with the retrieved context recorded alongside the response.
- Chunking and indexing tuned to your document types
- Permission-aware retrieval so answers respect access rules
- Citations back to the source record
AI agents and automation
Multi-step automation with declared tools, human approval where consequences are real, and a full replayable trace.
- Explicit tool and scope declarations
- Human-in-the-loop gates on irreversible actions
- Fail-closed behaviour on ambiguity
AI API integrations
Speech, vision and embedding services connected through the same audited path as any other integration.
- Declarative, reviewable mappings
- Retry, timeout and degradation handling
- Regional processing where residency matters
A sequence, not a discovery phase.
Specification
The task, the acceptance bar, the cost ceiling and the fallback behaviour.
Grounding
Data sources indexed with permissions and lineage intact.
Build
Features delivered behind evaluation, not straight to all users.
Evaluate
Output measured against the stated bar on your own examples.
Operate
Traces, budgets, dashboards and direct engineer access.
Answered without a call.
Does our data train a public model?
Not by default. Retention and training settings are specified per provider and written into the engagement.
How do you know the AI feature is good enough?
Against an acceptance bar agreed in the specification and measured on your own examples, not on generic benchmarks.
Can AI features respect existing permissions?
Yes. Retrieval runs under the requesting user's access rules, so answers cannot leak records they could not open.
What happens if a provider goes down?
The specification defines the degraded path — an alternate provider, a cached response, or a clean unavailable state.
Where this connects.
Most engagements combine two or three of these. Start wherever the pressure is highest.
Thirty minutes with an engineer, not a rep.
Bring your stack and your numbers. You leave with something useful either way.
- Your current stack, volumes and failure points — reviewed live
- An architecture sketch you keep, in your inbox the same day
- A firm CAD price for your exact specification, no follow-up gate
Free · 30 minutes · Video or phone · Reschedule any time