Define scope
Problem framing, data boundaries, risk policy.
baciu.com service area
AI discovery sprint
A short, evidence-led engagement for finding the workflows, data surfaces, owners, and risks that justify a production AI program.
How we approach it
We start with the business process, the users, and the failure modes. Then we choose the smallest architecture that can be measured, reviewed, and operated safely.
Explore pageWhat good looks like
A good AI system leaves traces: source evidence, evaluation history, cost and latency telemetry, and clear escalation rules for the cases that should not be automated.
Explore pageScope with operational clarity
- Discovery, pilot, production, and enablement engagement patterns.
- Decision rituals for scope changes, risk review, and release readiness.
- Commercial structures tied to measurable delivery outcomes.
Subject expansion
Pages that drill deeper into this delivery surface
Engagement models
How project scopes, delivery cadences, and ownership models are shaped for AI implementation work.
Explore pageDelivery governance
Governance practices used during implementation to keep velocity and risk in balance.
Explore pagePrototype-to-production engagement
A delivery path for turning an AI prototype into an operated service with permissions, evaluations, telemetry, release gates, and owners.
Explore pageProduction rescue
A stabilization path for AI systems already in use but suffering from quality drift, runaway cost, weak ownership, or broken handoffs.
Explore pageAI platform advisory
Advisory support for platform teams choosing architecture, orchestration, governance, data boundaries, and operating models for AI at scale.
Explore pageEvaluation and red-team engagement
A focused engagement for designing evaluation suites, adversarial scenarios, release thresholds, and quality evidence for high-impact AI systems.
Explore pageIntegration architecture review
A technical review for teams connecting AI systems to ticketing, ERP, CRM, identity, data warehouses, collaboration tools, and internal APIs.
Explore pageAI operating model design
A design engagement for assigning AI ownership, review rituals, release authority, support paths, cost controls, and post-launch improvement loops.
Explore pageCommand surface
Interactive control room for AI delivery.
Switch between architecture mapping, operating scenarios, and release-readiness checks.
Architecture lanes
Engineer core
Agent systems, reasoning, retrieval, action.
Control and trust
Governance, observability, incident response.
Scale operations
Delivery cadence, handoff, account operation.
Delivery atlas
Advanced navigator for capabilities, programs, and systems.
Filter, compare, and jump into detailed pages for AI architecture, execution, and governance.
Implementation library
AI enablement program
Enablement work for client teams that need to operate, govern, improve, and explain AI services after implementation support tapers.
AI operating model design
A design engagement for assigning AI ownership, review rituals, release authority, support paths, cost controls, and post-launch improvement loops.
AI platform advisory
Advisory support for platform teams choosing architecture, orchestration, governance, data boundaries, and operating models for AI at scale.
Delivery governance
Governance practices used during implementation to keep velocity and risk in balance.
Engagement models
How project scopes, delivery cadences, and ownership models are shaped for AI implementation work.
Evaluation and red-team engagement
A focused engagement for designing evaluation suites, adversarial scenarios, release thresholds, and quality evidence for high-impact AI systems.
Integration architecture review
A technical review for teams connecting AI systems to ticketing, ERP, CRM, identity, data warehouses, collaboration tools, and internal APIs.
Production rescue
A stabilization path for AI systems already in use but suffering from quality drift, runaway cost, weak ownership, or broken handoffs.
Prototype-to-production engagement
A delivery path for turning an AI prototype into an operated service with permissions, evaluations, telemetry, release gates, and owners.
Agent readiness assessment
A structured assessment for deciding whether a workflow is ready for autonomous or semi-autonomous execution.
AI implementation playbooks
Reusable delivery playbooks for moving from executive intent to working AI systems with clear ownership.
Case study library
A focused library of AI deployment stories showing the problem, system design, controls, and operating outcome for common enterprise environments.
Case study: financial services knowledge assistant
A regulated knowledge assistant pattern for analysts and service teams that need source-grounded answers, permission checks, and reviewable audit trails.
Case study: financial-services knowledge operations
An ActiveMotion-compatible case-study route showing how regulated knowledge work can move faster without weakening permissions, evidence, or review.
Case study: healthcare operations automation
An ActiveMotion-compatible case-study route for healthcare operations teams separating administrative support from clinical decision-making.
Case study: healthcare operations triage
An administrative triage pattern for routing intake, documentation, and follow-up work while keeping clinical judgment outside automation boundaries.
Case study: insurance claims modernization
A claims modernization pattern for using AI to prepare evidence, summarize loss details, surface coverage constraints, and route exceptions without hiding adjuster judgment.
Case study: logistics exception control tower
A logistics control-tower pattern for detecting shipment, inventory, supplier, and carrier exceptions early enough for planners to protect commitments.
Case study: manufacturing AI deployment
An ActiveMotion-compatible case-study route for manufacturing teams using AI to coordinate maintenance, quality, supply, and shift operations.
Case study: manufacturing maintenance intelligence
A plant-operations pattern for turning maintenance logs, manuals, quality records, and supplier notes into repeatable decisions.
Case study: professional services research workflow
A knowledge-work pattern for expert teams using AI to accelerate research, drafting, review, and reusable delivery assets.
Case study: public-sector service desk modernization
A service-desk modernization pattern for public organizations that need faster routing, policy-consistent responses, and visible accountability.
Case study: retail operations intelligence
A distributed-operations pattern for using AI to detect recurring store issues, guide frontline teams, and escalate exceptions with context.
Case study: telecom service assurance
A service-assurance pattern for correlating network events, customer cases, field dispatches, and change history into faster, more accountable incident resolution.
Case study: utility field-service readiness
A regulated field-service pattern for preparing crews, operators, and service teams with asset context, safety procedures, outage history, and escalation-ready evidence.
Change management cadence playbook
Operating cadence playbook for AI programs that need sustained adoption beyond launch milestones.
Client work
How we think about measurable production outcomes for teams adopting AI.
Control-plane design playbook
A playbook for designing the governance, observability, and release surfaces that make AI systems operable.
Customer pattern: energy and utilities
A regulated utility environment where AI supports outage coordination, asset maintenance, field-service readiness, and customer-program operations without weakening operator accountability.
Customer pattern: healthcare operations
A healthcare operations setting where AI helps administrative teams triage work, prepare context, and coordinate follow-up without entering clinical judgment.
Customer pattern: insurance operations
An insurance environment where AI supports claims, underwriting operations, policy servicing, broker workflows, and regulated customer communications with visible evidence.
Customer pattern: logistics and supply chain
A logistics and supply-chain environment where AI helps planners, warehouse teams, carriers, and service teams resolve shipment, inventory, and supplier exceptions faster.
Customer pattern: manufacturing operations
A manufacturing environment where AI turns maintenance logs, manuals, inspections, and supplier records into operational intelligence for frontline teams.
Customer pattern: professional services
An expert-services environment where AI accelerates research, drafting, delivery reuse, and client reporting while preserving professional judgment.
Customer pattern: public-sector service desk
A public-sector support environment where AI improves service-desk routing, knowledge access, and response consistency under explicit accountability constraints.
Customer pattern: regulated financial services
A customer environment where AI must support analysts and service teams without weakening auditability, permission controls, or reviewer accountability.
Execution lab
Interactive planner for AI implementation roadmaps.
Tune delivery tempo, autonomy, and risk profile to inspect recommended phases, dependencies, and control gates.
Risk profile
Delivery tempo
Recommended phases
W1+2
Executive AI roadmap
Strategy with an implementation path
W3+3
Engagement models
Scope with operational clarity
W6+4
Delivery governance
Governance in the delivery loop
W10+3
Production hardening playbook
Pilot to production with fewer regressions
W13+2
Studio delivery model
Delivery designed for durable ownership
W15+2
Enablement and handoff
Client teams can operate independently
Capability radar
Interactive map of AI implementation priorities.
Select an operating perspective and horizon to inspect relevant tracks, signals, and linked decision pages.
Priority tracks
AI enablement program
Teams ready to operate the system
Open pageAI implementation playbooks
Delivery is a system
Open pageClient work
Production-first delivery
Open pageExecutive AI roadmap
Strategy with an implementation path
Open pageDelivery governance
Governance in the delivery loop
Open pageStudio delivery model
Delivery designed for durable ownership
Open pageExecution blueprint
How this capability expands into production service.
Each area is delivered through explicit definition, measurable validation, and operating governance that client teams can inherit.
Operating checklist
What gets delivered with this work.
01
Architecture
A clear system map covering models, tools, data, workflows, users, and failure modes.
Explore page02
Evaluations
Task sets, regression checks, and release criteria for measurable AI behavior.
Explore page03
Controls
Human approval, access, logging, data-boundary, and incident-response rules.
Explore page04
Handoff
Documentation and ownership so the client can operate the system after launch.
Explore pageOperating risks to control
- Expanding autonomous authority without calibrated approval policies.
- Stale or conflicting sources that silently degrade decision quality.
- Insufficient traceability for automated actions and human interventions.
- Release processes that skip relevant regression scenarios.
Frequent questions
How do we choose where automation starts?
Start with repetitive, reversible workflows where outcomes and failure boundaries can be measured.
How do we prove quality before launch?
Use eval sets, adversarial scenarios, and explicit go/no-go criteria tied to business impact.
How does the team stay in control?
With authority boundaries, confidence thresholds, escalation packets, and complete execution traces.
What happens when model behavior changes?
Treat model and prompt changes as releases: test, review, approve, and roll out with rollback paths.
Coverage map
Companion pages for this delivery area
Engagement models
How project scopes, delivery cadences, and ownership models are shaped for AI implementation work.
Explore pageDelivery governance
Governance practices used during implementation to keep velocity and risk in balance.
Explore pagePrototype-to-production engagement
A delivery path for turning an AI prototype into an operated service with permissions, evaluations, telemetry, release gates, and owners.
Explore pageProduction rescue
A stabilization path for AI systems already in use but suffering from quality drift, runaway cost, weak ownership, or broken handoffs.
Explore pageRelevant pages
Representative AI deployments, rewritten as reusable patterns.
Case study: financial-services knowledge operations
An ActiveMotion-compatible case-study route showing how regulated knowledge work can move faster without weakening permissions, evidence, or review.
Explore pageCase study: healthcare operations automation
An ActiveMotion-compatible case-study route for healthcare operations teams separating administrative support from clinical decision-making.
Explore pageCase study: manufacturing AI deployment
An ActiveMotion-compatible case-study route for manufacturing teams using AI to coordinate maintenance, quality, supply, and shift operations.
Explore page