Bespoke AI applications
Internal tools and customer-facing applications designed around a specific operating problem and user experience.
Core Service
Custom AI Development
Purpose-built AI systems engineered around your workflows, data, users, integrations, controls, and operating requirements. This is the core of KeenSight's delivery model.
Starting investment: $75,000+ for production implementations
When the workflow matters too much for an off-the-shelf product
Custom AI development is the right fit when your process spans multiple systems, depends on proprietary data or business logic, requires differentiated user experience, or needs explicit security, governance, and human-review controls. We design the system around the way your organization actually operates rather than asking your organization to adapt to a generic AI product.
Before a wider technology commitment, review platform and architecture choices. Where the organization needs decision rights and ongoing ownership, establish production accountability alongside implementation.
Good fit when
- ✓The workflow is differentiated, complex, or strategically important.
- ✓The solution must connect to existing SaaS, APIs, databases, documents, or internal systems.
- ✓You need production-grade permissions, evaluation, auditability, exception handling, or human review.
- ✓A prototype exists, but it needs to become a reliable production system.
- ✓You want to own a tailored capability rather than license another generic platform.
What this engagement creates
Outcomes built around your operating environment
Agentic workflows
Single-agent or multi-agent systems that reason, retrieve, act, coordinate, and escalate within defined business boundaries.
Enterprise integrations
Connections to the systems your teams already use, including APIs, data stores, document repositories, SaaS platforms, and internal services.
Governed automation
Permissions, human review, exception paths, logging, evaluation, and control mechanisms designed into the workflow.
Production architecture
Software architecture, model selection, data flows, observability, deployment, and operational design appropriate to the use case.
Measured outcomes
Evaluation criteria tied to the workflow: quality, throughput, accuracy, cycle time, cost, adoption, or other business measures.
Typical deliverables
What you can expect
- ✓Solution architecture and implementation plan
- ✓Production application, workflow, or agent system
- ✓Required data and enterprise integrations
- ✓Evaluation framework and acceptance criteria
- ✓Human-review, escalation, and exception handling
- ✓Security, permissions, logging, and operational controls
- ✓Deployment, documentation, and handoff
- ✓Optional managed support and ongoing engineering
How we work
1
Frame the operating problem
Define the users, workflow, business objective, systems involved, constraints, and success measures.
2
Design the solution
Translate the operating problem into architecture, data flows, model boundaries, integrations, controls, and delivery scope.
3
Build and evaluate
Develop iteratively against representative workflows, data, edge cases, and acceptance criteria.
4
Deploy and operationalize
Integrate the system into production with monitoring, permissions, governance, documentation, and team handoff.
Start with the business problem
If the problem is well defined, we can scope a direct implementation. If important workflow or architecture questions remain, we may recommend a Blueprint or bounded Production Pilot first.
Schedule a consultationContinue with practical guidance
- AI Agent Architecture: Graph Workflows, Loops, Tools, and Control
Design AI agents with explicit graphs, bounded loops, narrow tools, durable business state, review controls, and outcome-based evaluation.
- Long-Running AI Agents: Durable Workflows, State, Retries, and Recovery
Design long-running AI agents with durable state, operation identities, bounded retries, approval records, reconciliation, and tested recovery.
- Multi-Agent Systems: When Multiple AI Agents Actually Help
Decide when multiple AI agents help by testing task decomposition, evidence contracts, coordination, shared state, and cost against a simpler baseline.
- RAG vs. GraphRAG: When Knowledge Graphs Improve AI Retrieval
Compare RAG and GraphRAG by query type, evidence quality, access, graph maintenance, freshness, and application-level evaluation.