Use cases & portfolio
Tasks, users, consequence, autonomy, integration, readiness, value, and ownership.
For AI buyers & adopters
Choose an AI platform path people can actually use.
Move from scattered prototypes and tools to a platform decision that connects use cases, model access, enterprise knowledge, agents, evaluation, safety, adoption, and accountable cost.
Buyer problem
More AI tools do not create a platform.
Teams accumulate model APIs, copilots, vector stores, gateways, agent frameworks, and observability tools while production readiness, user trust, operating ownership, and unit economics remain unclear. The real decision is what to standardize, reuse, source, integrate, or leave product-specific.
Discovery & advisory entry
AI Platform Decision Sprint
In a two-to-four-week planning range, map the use-case portfolio, current estate, constraints, risk, adoption needs, and economics; define required capabilities; evaluate reuse/buy/build options; and produce a decision and FDE-ready roadmap.
- Use-case, workflow, owner, and value map
- Capability and current/target architecture
- Shortlist and task-specific evaluation plan
- Adoption, cost, risk, and operating requirements
- Decision record and implementation boundary
Platform decision landscape
Decide capabilities before products.
Model access
Approved providers, gateway, identity, routing, quotas, versions, data boundaries, and fallback.
Knowledge & RAG
Authoritative sources, permissions, freshness, provenance, deletion, and measured retrieval.
Agents & tools
Explicit tools, least privilege, bounded state, approvals, intervention, and reconstructable actions.
Evaluation & safety
Versioned scenarios, traces, thresholds, guardrails, human review, incidents, and change triggers.
Adoption & cost
User fit, training, feedback, support, attributable usage, unit economics, and value evidence.
Select / Source
Make a documented platform decision.
Evaluate capability fit, integration, data boundaries, evidence, identity, operational maturity, adoption, cost, portability, support, and commercial terms. The result may be reuse, consolidation, purchase, a thin internal service, or no new platform.
Commercial boundary: Scott supports platform selection and sourcing. Resale, procurement rights, pricing authority, or vendor relationship claims apply only where current written authorization exists.
Implement via FDE
Turn the decision into working, governed AI.
FDE scopes the implementation arm: integrate platform services, migrate workloads, build AI applications, retrieval and agent workflows, connect enterprise tools, implement evaluation and telemetry, establish acceptance evidence, train owners, and transfer operations.
Explore Forward Deployed Engineering →Architecture and integration boundaryDelivery backlog and dependenciesData, model, retrieval, and agent recordsEvaluation, safety, cost, and acceptanceAdoption, support, and transfer plan
Proportionate governance & evidence
Control the consequential parts without leading with bureaucracy.
Use ownership, risk tiers, data permissions, evaluation, release evidence, human oversight, incident and exception paths, monitoring, and change review in proportion to the workflow. Governance supports delivery; higher-consequence needs can move into the specialist Governance & Assurance track.
Outcomes & deliverables
A decision package implementation can trust.
Prioritized use-case portfolioAI platform capability mapCurrent and target architectureEvidence-backed shortlist and evaluationAdoption and unit-cost frameworkOperating and control requirementsFDE delivery brief and roadmapDecision record with alternatives and conditions
Discover
Advise
Select / Source
Implement via FDE
Adopt / Optimize / Expand
Start with one consequential workflow