AI agency for companies — advisory, risk model and governance before implementation.

An AI agency owns the decision layer above implementation: which use cases are worth pursuing, in what order, with what risk class, on what stack, and under what governance. It is structurally different from an AI implementation partner (who ships one agent) and from a generalist consultancy (who delivers a slide deck). The deliverables are a prioritised use-case portfolio, a per-case risk and build-vs-buy decision, a governance operating model, and a monthly review cadence — not a single dashboard or a single agent.

(1) Portfolio prioritisation — score and rank candidate use cases on hours-returned potential, data sensitivity, change-management cost and reversibility. (2) Build-vs-buy-vs-agent — per case, decide whether an existing SaaS, a Hasty-built agent, or an internal build is the right answer, and document the trade-off. (3) Risk and compliance — classify each use case under the EU AI Act, map GDPR data flows, and define the human-in-the-loop checkpoints required. (4) Governance cadence — monthly review of live use cases, incident log, candidate pipeline and retirement of underperforming work.

Every candidate use case is classified before scope: minimal-risk (productivity helpers, summarisation), limited-risk (customer-facing assistants with disclosure obligations), high-risk (credit, hiring, critical infrastructure) and prohibited. High-risk cases require formal documentation, conformity assessment and a logged human-in-the-loop checkpoint on every decision — Hasty either delivers that scope correctly or declines the engagement. Prohibited cases (social scoring, real-time biometric in public spaces) are refused on intake. The point of the model is not to slow projects down; it is to prevent shipping the use case that becomes a regulator question 18 months later.

Governance is delivered as a recurring monthly review with the customer's owner: live use cases (status, hours returned, error rate, incidents), candidate pipeline (next two scored and risk-classed), retirement list (use cases not earning their keep), and any regulatory or model changes that affect classification. The output of each session is a one-page decision log signed by the customer owner — not a 40-page deliverable. AI moves quarterly; governance has to move monthly to keep up.

An AI agency is measured at portfolio level: percentage of prioritised use cases shipped within 90 days of scoping, total hours returned per week across the portfolio, incident count (customer-facing or compliance), and retirement rate (use cases honestly killed rather than kept on a roadmap). A working portfolio retires roughly one use case for every three it ships — anything below that means the agency is hoarding sunk cost.

Hasty's portfolio review is a structured conversation about the two or three AI use cases currently on the customer's table — what they would return, what they would risk, and which one is actually worth doing first. It is not a demo. The output is a one-page recommendation, not a proposal.

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