The work we cover
We help venture-backed companies turn an AI product or customer deployment into something the internal team can operate and repeat. The work starts with a specific growth constraint: implementation effort, unreliable outputs, slow responses, rising operating cost, or product work concentrated in one founder.
Our scope includes application engineering, customer integrations, evaluation, production permissions, tenancy, monitoring, and release controls. We work with your existing product and engineering leaders on the parts that block a defined outcome. A bounded engagement can add delivery capacity while leaving product direction and architectural ownership clear.
Forward deployed work connects engineering with customer operations. It can reveal what a customer needs to reach production and which part should become reusable product capability. We distinguish that work from indefinite custom development for one account. Both consume engineering time, and their commercial treatment needs to be visible.
How we deliver
We inspect the product, its use, and the delivery path. A walkthrough includes real examples, failures, manual interventions, deployment steps, and support requests. We identify where growth creates extra work and whether the constraint belongs in the core product, an integration, a process, or customer onboarding.
The delivery plan names the problem, internal owner, release criteria, and dependencies. We can build a specific capability, prepare a production release, or work alongside your team on customer integration. Access, review, code ownership, and handover are agreed before implementation.
For AI behaviour, we develop representative acceptance cases and test the full application. We check retrieved context, tool actions, tenant boundaries, model failure, and recovery alongside the quality of the generated output. A result that is accurate in one account must not expose another customer's information or depend on hidden manual correction.
For scaling, we examine latency, concurrency, usage limits, queues, retries, caching, and cost. We compare options using the same workload and quality criteria. Load testing needs realistic task mix and burst patterns, including failures, so a throughput number does not hide growing queues or repeated actions.
An illustrative engagement
This is a proposed engagement, not a measured Waypoint client case. A venture-backed software company has a working document assistant and several enterprise customers. Each deployment needs extensive founder involvement to map permissions, connect records, and repair customer-specific behaviour.
We trace a recent deployment and separate shared requirements from local configuration. The first scope creates a reusable connector boundary, a repeatable permission check, and an acceptance pack for onboarding. Customer-specific logic has an explicit place and owner instead of becoming an undocumented branch in the product.
We then run the next agreed deployment with the internal engineer using the new process. The review records the work still requiring specialist judgement, the components reused, and the support issues after release. The founder's reduced involvement is assessed through actual delivery and recovery work rather than an assumption that documentation alone removes the dependency.
How we measure value
We measure the constraint that justified the engagement. For customer delivery, that includes time to production, engineering effort per account, repeated configuration work, and post-launch support. For a product capability, it includes accepted task completion, quality, latency, and actual customer use.
Unit economics include inference, retrieval, hosting, failed attempts, human intervention, and ongoing delivery effort. We identify which costs grow with tasks, customers, or complexity. A lower token price is useful only if the completed task meets the same quality and operating requirements.
The growth case distinguishes observed use from an untested forecast. We can model higher load and test defined limits, but a successful test does not guarantee product-market fit or customer retention. We record what the evidence establishes and the next assumption that needs validation.
Simplicity also affects operation. Anthropic's agent design guidance describes cost and latency trade-offs from more complex agent systems. We assess those trade-offs in your application, with the product owner deciding which behaviour customers need and can support economically.
What your team owns
Your engineering team receives the agreed code, architecture decisions, tests, evaluation cases, deployment instructions, and operational documentation. Changes pass through the review and release process agreed with your technical lead. We record supplier dependencies and configuration that affects quality or cost.
Handover includes a practical operating exercise where appropriate: an internal engineer releases a change, diagnoses a failure, or onboards a customer using the documented process. Ongoing embedded delivery has defined allocation, priorities, reviews, and notice terms. Support commitments are agreed explicitly rather than inferred from an engineering engagement.
Questions before you start
Can you join our existing team? Yes. We can scope a delivery project or embedded work with a named internal owner. The first discussion establishes the constraint, access, decision rights, and review process.
Do you build prototypes or production systems? We can scope either, but their acceptance criteria differ. A feasibility prototype answers a bounded question. Production delivery includes the access, evaluation, reliability, and operating work required by real users.
Is this an investor assessment? This service is for operating-company delivery. Investment teams examining a target's claims need AI technical due diligence. Companies changing an internal process can start with AI implementation.