Build beyond the prototype
AI Development
Custom AI application development for your users, workflows and approved data. Build focused internal tools and knowledge systems with clear operational limits.
Discuss your project
What you receive
- Working application
- An internal tool, knowledge system or constrained agent connected to your interfaces, APIs and approved data sources.
- Evaluation suite
- Representative tasks and failure cases that check response quality, retrieval, latency and operating cost before release.
- Production handover
- Deployment configuration, monitoring, recovery instructions and documented ownership for maintenance and future model changes.
How we work
Specify
Agree on users, allowed actions and acceptance criteria.
Build
Connect one complete user journey to real data.
Harden
Test failures and permissions before expanding access.
Where this helps
- Internal search and knowledge tools that must answer from approved company sources.
- Document processing and review applications with structured outputs and visible source evidence.
- Constrained agents that can use selected tools while keeping consequential actions under human approval.
Systems and inputs
A build can connect to approved APIs, databases, document stores, identity systems and existing interfaces. The architecture follows the access model and operational environment you already need to support.
What shapes scope and cost
- The number, quality and maturity of the integrations required for a complete user journey.
- Permission boundaries, human approval steps and the consequence of an incorrect output or action.
- Evaluation coverage, deployment constraints, monitoring and the handover expected after release.
For defined use cases that standard software cannot support. Permissions live in application code, not prompts; consequential actions require human approval.
Questions teams ask
- Could off-the-shelf software be enough?
- Possibly. We compare standard products with a focused build before treating custom development as the default.
- How do you handle unreliable model output?
- Representative evaluations, source evidence, bounded actions and the ability to decline uncertain work are designed into the application.
- What do we receive at handover?
- The working application, deployment configuration, tests, evaluation assets and operating instructions needed for continued ownership.
Case study
Patent Data Lake
Application lookup and source access through a research portal and a controlled API.
Start with your context.
Share a representative task, existing systems and deployment constraints. We will shape the smallest useful build together.
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