Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
For a SaaS business, an AI feature has to lift activation, retention, and expansion, not just ship. We build copilots, embedded LLMs, and autonomous workflows into your product, with the evals and observability to prove they move the metrics your investors track.
From in-product copilots to eval infrastructure every engagement is built around shipping AI that earns its place in your product and justifies premium pricing.
Context-aware AI assistants embedded in your SaaS UI. Answer questions, automate tasks, and surface insights from within your product.
Autonomous agents that complete multi-step tasks on behalf of users: research, drafting, data entry, and cross-tool orchestration.
LLM-guided setup flows that reduce time-to-value. Users describe what they want; the AI configures it.
Natural language query over your users' data. "Show me last month's revenue broken down by product" answered without a BI tool.
Personalised content generation inside your product: reports, summaries, recommendations, and drafts tailored to each user's context.
The monitoring layer that tells you whether your AI features are working. Eval pipelines, quality scores, and regression alerts.
Without evals, you don't know your feature is degrading. We build the eval harness before the feature so you know it's working from day one.
System prompts change behaviour. They need version control, testing, and review like any other product code not ad hoc editing in a dashboard.
Keeping conversations coherent without blowing the context window requires architectural decisions most teams make too late. We design for this upfront.
Popular AI features can generate unexpected API costs. We design cost controls, caching layers, and model routing from day one not after the bill arrives.
We expose the AI feature via a backend API layer that your frontend calls. The copilot gets context from your product's data model user state, relevant records, and permissions without requiring a UI rewrite.
An eval harness is a test suite for your AI feature: a set of test cases with expected outputs, scored automatically on each deployment. It's the difference between knowing your feature works and hoping it works. We build this before writing the feature itself.
We implement prompt caching for repeated context, model routing (use a cheaper model when the task doesn't require a frontier model), response caching for deterministic queries, and token budget guardrails. Cost architecture is part of the design, not an afterthought.
Yes we typically run a one-week AI feature audit before any build. We review your product, your user data, and your competitors, then recommend the highest-ROI AI features with effort estimates and expected retention impact.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.