Generative AI consulting & engineering
BluePlane helps you choose where GenAI can create measurable value, then designs and builds the secure, evaluated, integrated system required to deliver it.
Ways to start
Each engagement ends in a useful decision or working capability. Scope expands only when the evidence supports it.
Turn a broad AI mandate into a ranked, technically credible opportunity with a clear investment decision.
Validate the highest-risk assumptions with real data, representative users, and measurable success criteria.
Engineer the surrounding product, workflow, and operating controls that turn a capable model into a dependable system.
What we can build
Good use cases have a real user, a bounded workflow, accessible context, and an outcome that can be measured.
Grounded search and question-answering across policies, product documentation, support content, and internal knowledge—with citations and access controls.
Extract, compare, summarize, classify, and route information from contracts, reports, forms, and other high-volume document workflows.
Assist people or execute bounded tasks across existing tools, APIs, and approval steps while keeping consequential decisions visible.
Add differentiated generation, analysis, personalization, or conversational experiences to an existing software product or a new one.
Built for production
A polished interface does not make an AI system ready. Production quality comes from controlling the whole path between a user’s intent, enterprise data, model behavior, and an operational outcome.
Define representative test cases, quality thresholds, failure categories, and human review before scaling adoption.
Protect data, prompts, tools, identities, and outputs with least privilege, isolation, logging, and explicit retention choices.
Use grounding, content controls, constrained actions, approvals, and safe failure behavior where the workflow requires them.
Track latency, model and retrieval quality, usage, failure modes, and unit cost—not just infrastructure uptime.
Document the architecture, evaluation system, operational procedures, and improvement loop so your team can run it.
The credential validates foundation-model integration, RAG, agents, responsible AI, evaluation, security, operations, and cost optimization.
Common questions
No. The Opportunity & Readiness Sprint is designed to turn a broad goal or set of ideas into a ranked use case and a clear decision. We will also say when conventional automation or search is the better answer.
Yes. Integration is central to the offer: identity, permissions, business data, APIs, user experience, approval steps, monitoring, and the operational team all need to work together.
No. The founder’s professional GenAI credential is from AWS, but recommendations follow your constraints. Model quality, privacy, portability, latency, cost, existing cloud investment, and team skills determine the architecture.
Yes, when it answers a consequential question. We define success and failure criteria first, build a representative vertical slice, and make an explicit recommendation about what should happen next.
Start with the workflow
Bring the opportunity, the stalled pilot, or the workflow you believe should work better. We will help define the smallest credible next step.
Discuss an AI opportunity