Build
Fixed-scope sprints to get working AI in your hands. You come in with a clear goal and leave with something running — a feature in production, an agent in staging, a deployed model, an eval harness in your CI.
What this tier is for
Build programs are for teams that know what they want and are ready to build it. Each one is fixed scope — the deliverable, the timeline, and the price range are agreed before work starts. These run for two to four weeks. You leave with something working, not a plan.
Build programs
Scope
Takes one defined AI feature from specification to production in 3 weeks, with monitoring and cost controls set up from day one.
What you get
One AI feature live in production with monitoring and cost controls
Scope
Builds a document processing pipeline in 4 weeks that extracts structured data from your documents and measures accuracy at the field level.
What you get
Production IDP pipeline with measured extraction accuracy per field
Scope
Designs, builds and tests a working AI agent in your staging environment over 2–3 weeks, with architecture documentation and an evaluation baseline.
What you get
Working agent prototype in staging with architecture docs and an evaluation baseline
Scope
Builds an evaluation harness connected to your CI pipeline in 4 weeks, with baseline scores configured for every metric you need to track.
What you get
Working eval harness in the client’s CI with baseline scores for every tracked metric
Scope
Deploys an open-weight model into your own infrastructure (VPC or on-premise) in 4 weeks, with throughput benchmarked and cost per call established.
What you get
Open-weight model in production in your VPC or on-premise, with benchmarked throughput and cost
Scope
Runs adversarial testing against your AI system over 2–3 weeks, probing for security vulnerabilities, prompt injection risks and failure modes.
What you get
Severity-ranked adversarial findings report with a remediation plan
Which to choose
if you want one specific AI feature shipped to production in three weeks.
if you have documents that need to be processed at scale and you need measurable extraction accuracy.
if you want a working agent in staging to validate your design and approach before scaling.
if you need a reliable way to measure whether your AI is performing consistently and you want it in your CI from day one.
if you need to run a model inside your own infrastructure rather than calling an external API.
if you want to find the security gaps and failure modes in your AI system before your users do.