Example one-pagers (copy/paste into separate files)

One-pager 1 — Agilarc AI Readiness & Data Foundation Sprint (save as: one-pager-ai-readiness.md)

What it is A fixed-scope assessment + action plan that turns “we want AI” into an executable backlog across data, security, and delivery.

Ideal buyers CIO / VP Data / BI Director / Enterprise Architect / Compliance + Data Governance leaders

Problems we solve

• AI pilots failing due to poor data quality, missing metadata, unclear ownership, and unclear “what data can go into AI”
• No prioritized plan to become “AI-ready” without boiling the ocean

What you get (deliverables)

• AI use-case shortlist (top 3–5) tied to measurable outcomes
• Data readiness scorecard (quality, completeness, accessibility, lineage/metadata, security posture)
• Target architecture options (Azure-centric if desired) + integration approach
• 30/60/90-day roadmap with effort sizing and quick wins
• “GenAI acceptable-use” starter pack (roles, controls, risk tiers, human-in-the-loop checkpoints)

Typical timeline 2–4 weeks

Commercial model Fixed-fee sprint (option to credit part of fee toward implementation)

Why Agilarc You already deliver data quality, metadata management, governance programs, and BI readiness—this sprint simply “AI-enables” what you do best. (agilarc.com)

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One-pager 2 — RAG-as-a-Service: Private Knowledge Assistant for SharePoint/Confluence/Files (save as: one-pager-rag.md)

What it is A managed Retrieval-Augmented Generation (RAG) solution so staff can ask questions across internal docs and get sourced answers.

Ideal buyers Operations, PMO, IT support, engineering teams, compliance-heavy orgs, regulated environments

Common use cases

• Policy & procedure Q&A (with citations to source docs)
• “How do we do X here?” onboarding assistant
• Ticket deflection + faster resolution via doc-grounded answers
• Vendor/SOW/contract clause lookup and summarization

What you get (deliverables)

• Document ingestion pipelines (connectors + scheduled refresh)
• Chunking/indexing strategy + relevance tuning
• Guardrails (PII filtering, role-based access, prompt/response policies)
• Feedback loop (thumbs up/down, answer quality review workflow)
• Usage + cost reporting dashboard

Typical timeline MVP in 3–6 weeks; then iterate.

Commercial model Build fee + monthly managed service (index refresh, monitoring, enhancements)

Differentiator You pair the assistant with the hard parts others skip: data quality, metadata, and governance so the assistant stays trustworthy over time. (agilarc.com)

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One-pager 3 — Natural-Language Analytics Copilot (NL→SQL + Power BI Enablement) (save as: one-pager-nl2sql.md)

What it is A governed analytics “copilot” that lets business users ask questions in plain English and receive validated results (with transparent SQL and semantic definitions).

Ideal buyers BI leaders, analytics teams, finance/ops leaders who want self-service without dashboard sprawl

Problems we solve

• BI bottlenecks (“we can’t build dashboards fast enough”)
• Metric confusion (“revenue” means 5 different things)
• High training cost for BI tools among non-technical users

What you get (deliverables)

• Metrics layer definition (business glossary + approved calculations)
• Curated query layer (views/tables) designed for safe generation
• NL→SQL workflow with constraints (row limits, approved schemas, query validation)
• Power BI integration pattern (where needed) + adoption playbook

Typical timeline 4–8 weeks depending on data readiness.

Commercial model Implementation project + optional ongoing tuning/monitoring

Why it fits Agilarc This is a direct extension of your BI readiness/maturity work and dashboard/reporting delivery—just with an AI interface on top. (agilarc.com)

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One-pager 4 — AI Governance, Monitoring & Enablement (Copilot/LLM Ops) (save as: one-pager-ai-governance.md)

What it is A pragmatic governance + measurement layer so AI usage is safe, auditable, and cost-controlled (especially important in enterprise and regulated contexts).

Ideal buyers CISO org, data governance council, CIO, compliance, platform engineering

What you get (deliverables)

• Model/tool inventory + approved-use matrix (by data classification)
• Prompt/workflow standards + logging requirements
• Evaluation harness (accuracy, hallucination risk checks where applicable)
• Cost/usage telemetry, alerting, and exception handling
• Training workshops for end users + power users (hands-on)

Typical timeline 2–6 weeks to stand up; then continuous improvement.

Commercial model Retainer / managed service (monthly)

Local proof of demand signal Regional institutions are explicitly rolling out “secure GenAI tools” and documenting acceptable-use expectations—governance and enablement are no longer optional. (technology.pitt.edu)