Services

Enterprise AI consulting services

Structured advisory and implementation services for organizations moving from experimentation to production-grade adoption.

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AI Readiness Assessment

Business problem

AI investment starts before process ownership, data quality, and integration constraints are clear.

What we do

We assess workflows, data, systems, and governance to prioritize feasible use cases and sequence investment.

Deliverables

Process maps and decision points
Data source maps and lineage sketches
Gap analysis across process, data, systems, governance
Readiness scoring with explicit assumptions
Prioritized use case shortlist
AI roadmap tied to controls and funding gates

Typical timeline: 2–4 weeks

Best fit: Leaders launching or resetting AI programs and requiring an objective baseline.

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AI Validation Sprint

Business problem

Teams commit to build before proving feasibility in live enterprise conditions.

What we do

We validate data quality, workflow fit, and integration feasibility, then issue a Go / No-Go recommendation.

Deliverables

Data quality and accessibility assessment
Prototype or simulation aligned to workflow reality
Feasibility report with risks and mitigations
Refined roadmap and funding recommendation
Explicit Go / No-Go decision package

Typical timeline: 2–4 weeks

Best fit: Organizations preparing to fund build work and needing evidence-backed validation.

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AI Pilot Implementation

Business problem

Pilots stall when outputs are not embedded into systems, roles, and KPIs.

What we do

We build integrated pilot workflows with controls, monitoring, and KPI tracking.

Deliverables

Pilot solution integrated into target workflows
System integrations and API/data contracts
Governance hooks: access, auditability, controls
KPI dashboard with baselines
Scale recommendation and operating playbook

Typical timeline: 4–8 weeks

Best fit: Teams with a validated use case ready for controlled production adoption.

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AI Governance & Operating Model

Business problem

AI adoption expands without clear ownership, controls, or review accountability.

What we do

We define ownership, controls, review paths, escalation, and monitoring for responsible scale.

Deliverables

Operating model and governance framework
Process redesign guardrails for AI-enabled workflows
Data governance alignment for AI consumption
Risk controls and escalation paths
Training and adoption plan

Typical timeline: 3–6 weeks (program-dependent)

Best fit: Regulated or distributed enterprises scaling beyond isolated pilots.

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Enterprise Integration Advisory

Business problem

AI outputs fail to drive decisions when core systems are poorly connected.

What we do

We define integration patterns, data contracts, and rollout sequencing across systems.

Deliverables

Integration architecture options and trade-offs
API/event backlog with priorities
Data contract outlines and quality gates
Security and access model alignment
Phased rollout plan with operational checkpoints

Typical timeline: 2–6 weeks

Best fit: Complex landscapes where workflow automation depends on dependable system handoffs.

AI Workflow Automation

Business problem

Teams automate tasks without redesigning workflows, creating brittle outputs and weak ROI.

What we do

We redesign workflows and place AI at decision points with human review where needed.

Deliverables

Target workflow blueprint
Automation scope with exception paths
Monitoring and quality checks
Runbooks for operators
Measurement plan tied to throughput and quality KPIs

Typical timeline: 4–10 weeks

Best fit: Operations-heavy functions needing throughput, consistency, and auditability.