AIStack Deliverables

A structured deliverables model that explains exactly how we move a client from pilot chaos to governed production AI operations.

Deliverable Track 0 - Rapid Discovery (1-3 sessions)

Map business intent, data reality, and current cost risk.

What We Map

  • Top 10 questions the business wants AI to answer, by team
  • Where truth lives: databases, CRM, docs, tickets, spreadsheets
  • Current governance and sensitivity boundaries
  • Cost baseline: usage, failures, retries, prompt size
  • Tool landscape: APIs, dashboards, ETL, warehouse/lake

Deliverables

  • AI Question Inventory
  • Data and Tool Map

Deliverable Track 1 - Metric + Semantic Standardization

Stop ambiguity before it reaches prompts and agents.

  • Revenue definitions: gross/net, refunds, currency conversion
  • Churn definitions: logo vs revenue, window
  • Active user definitions: last 7 vs 30 days

Deliverables

  • Metric Dictionary (human + machine readable)
  • Business glossary with synonyms and entity definitions
  • Question quality rules (what must be specified)

Token optimization: central definitions remove repetitive clarification loops.

Deliverable Track 2 - Agent-Ready Tool Layer

Governed tool calling instead of unrestricted database access.

Example tools

get_kpi(metric, timeframe, segment)
compare_kpi(metric, period_a, period_b, segment)
top_drivers(metric_change, dimensions, timeframe)
customer_risk_scores(segment, window)
search_docs(query, filters)

Key rules

  • Permission-scoped access by role
  • Minimal, structured JSON outputs
  • Deterministic errors to avoid reflection loops

Deliverables

  • Tool catalog with docs, examples, and permissions
  • JSON contracts for safe tool outputs
  • Audit logs for each tool call

Token win: less prompt stuffing and fewer retries.

Deliverable Track 3 - Retrieval + Context Compression

  • Hybrid retrieval with metadata filters + embeddings
  • Compression strategy: summarize, aggregate, extract entities
  • Citation retention for traceable answers

Deliverables

  • Retrieval policy by question type
  • Compression policy by payload type
  • Lean few-shot example store

Token win: reduced injected context, the main cost driver.

Deliverable Track 4 - Routing + Budget Guardrails

  • Small model for classification/simple SQL tasks
  • Strong model only for complex reasoning
  • Hard budgets: max context tokens and max tool calls
  • Clarification threshold when requests are ambiguous
  • Caching repeated question patterns

Deliverables

  • Model routing rules
  • Token budgets per team/use case
  • Cost spike alerting

Token win: prevents silent cost explosions.

Deliverable Track 5 - Evaluation + Observability

  • Log chain: prompt, retrieval, tool calls, output, feedback
  • Golden question test set with expected outcomes
  • Metrics: correctness, tool success, tokens per question, retries

Deliverables

  • Evaluation harness
  • Weekly agent report card
  • Continuous optimization backlog

Token win: systematically removes wasteful workflows.

Client Outcomes

  • Employees ask better questions
  • AI answers are more reliable
  • Costs become predictable
  • Data access is governed
  • Tools are reusable across teams
  • The system improves over time

AIStack Offer

1) AI Cost + Governance Audit (1-2 weeks)

Baseline, quick fixes, leakage map, and implementation roadmap.

2) Agent-Ready Layer (4-8 weeks)

Tools, permissions, retrieval, routing, budgets, and logging.

3) Continuous Optimization (monthly)

Evaluation loops, refinements, and safe onboarding of new use cases.

Meeting One-Liner

"We do not just plug in an LLM. We build a governed tool-and-catalog layer so AI can act safely with minimal context, reducing token costs while improving reliability."

Discuss your rollout