AI Strategy Consulting

Hire consultants when: (1) Lack internal AI expertise: Your team doesn't know LLMs from random forests. Consultants bring 100+ projects of experience. (2) Need unbiased perspective: Internal teams have biases (engineers want to build everything, execs fall for vendor hype). Consultants provide objective build-vs-buy analysis. (3) Speed matters: Consultants deliver in 4-12 weeks what takes internal teams 6-12 months (they know the pitfalls). (4) Political deadlock: When departments fight over AI budget, external consultants provide neutral prioritization framework. (5) High-stakes decisions: Investing $2M-$10M in AI? Spend $20K-$50K on consulting to avoid $2M mistakes. Do strategy in-house when: You have experienced AI/ML team, small budget (<$100K for AI), simple use case (buy existing SaaS), or plenty of time to experiment (startup in learning mode).

Typical ROI: 10x-100x (we help you avoid $200K-$5M mistakes OR identify $2M-$50M opportunities). Example 1 (Cost Avoidance): Manufacturing client almost bought $5M computer vision system. Our $18K consulting: analyzed ROI, negotiated pilot, proved $800K solution was sufficient. SAVED $4.2M (233x ROI on consulting fee). Example 2 (Opportunity Identification): E-commerce client had $1M AI budget, 15 competing ideas. Our $18K consulting: prioritized demand forecasting ($800K investment, $3M/year savings). ROI: $3M benefit vs $18K consulting = 167x return. Example 3 (Build vs Buy): Healthcare client deciding: Epic AI module ($4M over 3 years) vs custom build. Our $18K consulting: TCO analysis showed custom build costs $1.05M, saves $2.95M. ROI: 164x. Break-even question: If consulting helps you make ONE better decision (avoid $200K mistake OR capture $200K opportunity), ROI is 10x on $20K consulting fee. We typically find $1M-$10M in value.

Our prioritization framework (data-driven, not political): Step 1: Business Impact Scoring (0-10). Revenue increase: Calculate $ impact (A/B test estimates, market benchmarks). Cost savings: Quantify hours saved × hourly cost, defect reduction × cost per defect, etc. Strategic value: Competitive differentiation, customer satisfaction (NPS impact). Step 2: Technical Feasibility (0-10). Data availability: Do you have clean, labeled data? Model maturity: Existing solutions (high feasibility) vs novel research (low). Time to deploy: <3 months (high) vs >6 months (low). Step 3: Implementation Risk (0-10, inverse). Business criticality: Pilot vs core process. Change resistance: User adoption challenges. Regulatory: Compliance requirements. Step 4: Calculate Priority Score = (Impact × Feasibility) / Risk. Rank all use cases by score. Step 5: Stakeholder Alignment Workshop. Present scoring methodology (objective, data-driven). Show top 10 ranked use cases. Discuss disagreements (usually political, not analytical). Build consensus on top 3-5 for phased roadmap. This framework removes emotion/politics, focuses on business value.

We are 100% vendor-agnostic (we don't take vendor kickbacks/referral fees). Our recommendations are solely based on YOUR best interest: Technical fit: Does this tool solve your specific problem? Cost: Total Cost of Ownership (TCO) over 3 years, including hidden costs (implementation, training, support). Vendor stability: Will this vendor exist in 3 years? What if they get acquired? Lock-in risk: How hard to migrate if you want to switch? (Favor open standards, avoid proprietary lock-in.) Support quality: Reference checks with 3-5 current customers. Our typical recommendations: LLMs: GPT-4 (best reasoning, but expensive), Claude 3.5 (best writing, mid-price), Llama 4/Qwen3 (free, self-hosted, data privacy). We model all options with YOUR volume/use case. Vector DBs: ChromaDB (simple use cases), Qdrant (scalable, open-source, our default), Pinecone (managed, expensive, avoid if possible), Milvus (enterprise scale). MLOps: Start with free/open-source (MLflow), upgrade to managed (Weights & Biases) only if team >10 ML engineers. Cloud: We don't favor AWS/Azure/GCP. We analyze YOUR existing infrastructure, team skills, contractual commitments. We'll tell you if vendor X is overpriced for your needs. We negotiate contracts (save you 20-40%).

We offer both strategy-only and strategy+implementation. Strategy-Only Consulting (most clients start here): Deliverable: Roadmap, architecture, vendor recommendations, budget. You implement in-house or hire dev shop. Best for: You have engineering team, just need expert guidance on what to build. Strategy + Proof-of-Concept (PoC): We develop working prototype for 1 high-priority use case (8-10 weeks). Validates technical feasibility, proves ROI before full investment. Deliverable: PoC demo + go/no-go recommendation + production deployment plan. Best for: High-stakes decision (want to test before committing $500K+). Strategy + Full Implementation (separate engagement): After strategy phase, we can implement top-priority use cases. Typical engagement: 3-6 months, $100K-$500K (depending on complexity). Deliverable: Production-ready AI system, trained team, handoff. Best for: You lack internal AI expertise, need turnkey solution. Hybrid Model (common): We do strategy ($18K-$45K). You start implementation in-house. We provide ongoing advisory (monthly retainer $5K-$10K/month, 8-16 hours). Help with: technical questions, vendor negotiations, architecture reviews, hiring support.

Strict confidentiality (we sign NDAs, handle sensitive data securely): Legal Protections: Mutual NDA before any discussions (standard practice). Client owns all deliverables (reports, models, code—100% yours, we retain nothing). No case studies without written permission (we won't mention your company publicly). Data Handling: Minimize data access: We work with aggregated/anonymized data when possible. For PoCs: Sample data only (not full production datasets). Secure transfer: Encrypted file sharing (no email attachments), VPN access to your systems if needed. Data deletion: All client data deleted from our systems after engagement ends (we provide certification). On-Premise Work: Sensitive industries (healthcare, finance, government): We work on-site or via your secure infrastructure. No data leaves your network (we SSH into your servers, don't copy data out). Air-gapped environments: We can work in air-gapped networks (common for defense, financial trading). Examples: Healthcare: HIPAA compliance (BAA signed, ePHI handling procedures). We access EMR data via read-only on-prem terminal. Finance: Work in bank's secure facility, no laptops/phones allowed in data center. Government: Security clearance available (US Secret clearance for 2 senior consultants).

Yes—early-stage consulting (before you're ready to build) is often MOST valuable: Use Case 1: "Should we invest in AI at all?" You're AI-curious but unsure if it makes business sense. Our Opportunity Assessment ($5K, 1-2 weeks): We analyze your business, identify 10-15 AI opportunities, estimate ROI. Outcome: Clear yes/no answer. If "yes," you know where to start. If "no," you saved yourself from wasting $500K on failed AI experiments. Use Case 2: "We have $1M budget next year—how to prepare now?" You have budget in 6-12 months, want to lay groundwork. Our consulting (now): Data readiness assessment (start cleaning data now), technology landscape education (what's possible with AI today), use case scoping (so you can hit ground running when budget arrives). Outcome: When budget unlocked, you deploy in 2 months (vs 6 months if you start from scratch). Use Case 3: "We tried AI before and failed—should we try again?" You have AI PTSD (previous project failed, wasted $500K, leadership skeptical). Our consulting: Post-mortem analysis (why did it fail? wrong use case? bad vendor? poor change management?), identify low-risk pilot to rebuild trust (small budget, fast time-to-value), change management strategy. Outcome: Second attempt succeeds because you learned from mistakes. Bottom line: Consulting before you're "ready" de-risks future investment, avoids costly mistakes, educates leadership.

Success Metrics (defined upfront, agreed with client): Quality of Recommendations: Are they actionable? (Not vague "use AI for customer experience," but "deploy Llama-based chatbot for Tier 1 support, pilot in 8 weeks, $80K cost, $300K/year savings"). Are they data-driven? (ROI models with clear assumptions, not hand-waving). Stakeholder Alignment: Do executives agree on priorities after consulting? (Success = consensus on roadmap, not political deadlock.) Outcome-Focused: Our recommendations lead to successful implementations (we track: 80% of our strategy clients implement at least 1 recommended use case within 12 months, average ROI: 250%). What if you disagree with our recommendations? We welcome healthy debate (disagreement often means we're challenging assumptions—that's our job): Process: We present findings with data/evidence. We discuss concerns, answer questions. If you disagree, we explore why: missing context? different risk tolerance? political constraints we didn't know about? We revise recommendations if you provide new information that changes analysis. Final deliverable reflects YOUR decision (even if we advised differently—we document "consultant recommendation" vs "client decision"). Example: We recommended "build custom AI" ($100K), client chose "buy SaaS" ($50K/year) due to limited engineering resources. We documented both options, TCO comparison, and client's rationale. Follow-up: 6-12 months post-engagement, we check in: Did you implement? What worked? What didn't? This feedback loop improves our future recommendations. Our track record: 85% client satisfaction (would hire us again), 80% implementation rate (clients actually use our recommendations, not shelf-ware).