Production-ready AI services—from assessment to deployment

Problem: Teams jump into tools before aligning on ROI, data reality, and integration constraints—leading to stalled prototypes.

How AI solves it: We map high-leverage use cases, validate feasibility, and define success metrics and governance.

Deliverables:

• Use-case shortlist + ROI hypothesis
• Data/integration inventory
• Risk & security checklist
• Pilot scope + execution plan

Timeline: 5–10 business days

Best for teams that need clarity before investing in build-out.

Problem: LLM apps break in production due to hallucinations, data access gaps, and uncontrolled token costs.

How AI solves it: Retrieval-augmented generation (RAG), guardrails, evaluation, and cost/latency controls.

Deliverables:

• RAG pipeline (chunking, indexing, relevance tuning)
• Prompting + safety policy
• Evaluation set + regression checks
• Observability for quality and cost

Timeline: 2–6 weeks

Ideal for knowledge search, support deflection, internal copilots.

Problem: Support costs rise while resolution time increases—agents are overloaded with repetitive questions.

How AI solves it: A chatbot that answers accurately from your knowledge sources, escalates correctly, and improves over time.

Deliverables:

• Bot UX flows + escalation rules
• Knowledge ingestion + freshness strategy
• Analytics dashboard (deflection, CSAT, containment)
• Security: role-based access where needed

Timeline: 2–5 weeks

Built to reduce tickets—not just answer one-off prompts.

Problem: Forecasts and decisions are made on intuition or delayed reports—leading to missed targets and wasted spend.

How AI solves it: Forecasting and propensity models integrated into workflows so teams act faster with better confidence.

Deliverables:

• Model development + evaluation
• Feature engineering and data pipelines
• Deployment endpoint / batch scoring job
• Monitoring and retraining plan

Timeline: 3–8 weeks

Common outcomes: uplift in conversion, reduced churn, lower inventory risk.

Problem: Manual inspection and image review slows throughput and increases error rates.

How AI solves it: Vision models that detect defects, classify items, and flag anomalies—with human review where required.

Deliverables:

• Dataset strategy + labeling guidelines
• Model training + benchmarking
• Edge / cloud deployment plan
• Operational QA workflow

Timeline: 4–10 weeks

Best for manufacturing quality, warehouse verification, safety monitoring.

Problem: Models perform in notebooks but fail in production due to missing monitoring, brittle pipelines, and unclear ownership.

How AI solves it: A deployment system that makes quality repeatable: CI/CD, evaluation gates, and reliable observability.

Deliverables:

• Deployment architecture + environments
• Monitoring (quality, drift, latency, cost)
• Release process and rollback strategy
• Documentation + enablement

Timeline: 3–8 weeks

Often paired with LLM or predictive projects for fast time-to-production.

Problem: High-value teams lose time on repetitive tasks—triage, routing, summarization, and follow-ups.

How AI solves it: Automations that integrate with your systems and reduce cycle time while keeping controls and auditability.

Deliverables:

• Workflow mapping + automation candidates
• Integration with CRM/helpdesk/docs
• Human-in-the-loop controls
• Reporting on time saved and quality

Timeline: 2–6 weeks

Best for support ops, sales ops, finance ops, and internal enablement.