AI Consulting Services
AISD's AI consulting produces opinionated architectures, prioritized roadmaps, and honest build-vs-buy decisions. Every engagement is fixed-scope, fixed-price, and ends with deliverables you can act on — usually a fixed-price build proposal.
$8,000 discovery sprints · 4–8 wk strategic engagements · No retainer-style busywork
What's included
01
A 12-month roadmap with prioritized initiatives, ROI estimates, and dependency mapping. Anchored to specific business outcomes — revenue impact, cost reduction, throughput, NPS — not vague 'transformation.'
02
Opinionated reference architecture for your top initiatives. Model selection, retrieval pattern, agent orchestration, eval design — chosen for the actual workload, not for novelty.
03
Review of existing AI workloads for cost, reliability, prompt-injection exposure, and ROI. Outputs a prioritized fix list — not a generic risk assessment.
04
Honest assessment of off-the-shelf AI tools versus custom builds for your specific workload. We have no vendor commissions; recommendations are based on what works in production at your scale.
Engagement formats
1–2 weeks
FROM
$8,000
Scoped to a single initiative. Outputs a written architecture, throwaway prototype, and fixed-price build proposal.
Best for
Single project scoping, vendor evaluations, or build-vs-buy decisions.
4–8 weeks
FROM
$25,000
Full strategy + architecture engagement covering 5–15 initiatives, with the top three architected to build-ready depth.
Best for
Series A–C teams setting their AI roadmap or mid-market enterprises consolidating scattered AI work.
2–4 weeks
FROM
$15,000
Review of existing AI workloads with a prioritized fix list and per-fix effort estimates.
Best for
Teams with AI in production but no eval harness, no observability, or unexplained cost growth.
Methodology
Most AI consulting companies sell either pure strategy (slideware) or pure delivery (heads-down build). The pattern that works for mid-market and enterprise teams is a tight sequence: define the metric, validate the riskiest assumption with a throwaway prototype, then commit to a fixed-scope build only if the prototype survives. That sequence is what we run on every engagement.
The four operating principles behind it:
Sample audit output
Excerpted from a real (anonymized) audit deliverable. The full version is ~25 pages including diagrams; here's the structure:
Section 1 - Inventory
Every AI workload in production. Model + version, monthly cost, latency p95, owner, last-eval-run date. Tabular, exportable for procurement review.
Section 2 - Cost analysis
Per-workload cost breakdown vs baseline. Found in past audits: 40-70% inference savings unlocked by prompt caching + model routing. Concrete numbers per workload, not hand-waving.
Section 3 - Reliability
Eval coverage map per workload. Drift detection. Known failure modes. Most-common gap: 60-70% of workloads have no eval harness; reliability is asserted, not measured.
Section 4 - Security
Prompt-injection exposure per workload. PII / PHI / PCI flow paths. Audit-log completeness for regulatory review. Adversarial test suite recommendations.
Section 5 - Prioritized fix list
Every finding scored on impact × effort. Top 10 ranked. Each with effort estimate (engineer-weeks) so your team can plan the work themselves OR contract AISD to ship it.
The audit itself is fixed-scope, fixed-price ($15,000-$25,000 depending on workload count), and ships in 2-4 weeks. We don't sell follow-on retainers - the fix list goes to your team or becomes a separate engagement scoped against the prioritized list.
How AISD compares to other AI consulting firms
The AI consulting market spans three tiers. Each fits a different buyer.
Tier 1 - Big-4 strategic consulting
McKinsey QuantumBlack, BCG GAMMA / BCG X, Deloitte AI. Best for $1M+ Fortune 500 mandates where the deliverable is organizational change. They don't ship code.
Tier 2 - Large AI agencies
LeewayHertz, Quantiphi, Markovate. 200-3,000 engineer organizations. Mixed seniority. Account-manager layers. Hidden pricing. Best for enterprise budgets and multi-quarter timelines.
Tier 3 - Senior-only specialty firms (where AISD lives)
AISD, Tribe.ai, Faculty AI. Smaller teams. Senior-only or network-of-contractors model. Public or transparent pricing. Best for teams shipping their first or second production AI feature in 4-12 weeks who want a partner that lives and breathes AI engineering.
See our Best AI Consulting Companies 2026 guide for honest rankings of 8 firms across all three tiers, with pricing bands and best-for use cases for each.
What we don't do
Then ship the build
Industries we consult into
Outcomes from past consulting
Frequently asked
AI consulting at AISD covers four work streams. Strategy: AI roadmap aligned to business outcomes, prioritized by ROI and feasibility. Architecture: model selection, retrieval pattern, agent orchestration, eval design — opinionated based on what actually works in production. Build-vs-buy: clear decisions on which problems to solve with off-the-shelf AI, custom builds, or no AI at all. Audit: review of existing AI workloads for cost, reliability, prompt-injection exposure, and ROI.
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.
Two formats. Discovery sprint: $8,000–$18,000 for 1–2 weeks, produces a written architecture, throwaway prototype, and fixed-price build quote. Strategic engagement: $25,000–$75,000 for 4–8 weeks, produces a 12-month AI roadmap, prioritized initiative list, and architecture for the top three. Both are paid up front, fixed-scope. We do not run open-ended advisory retainers with no deliverables.
RAG (retrieval-augmented generation) grounds a model's response in external data — used when answers must be current or proprietary. Fine-tuning changes model weights to teach a specific style or domain — used when prompts can't reliably elicit the behavior. Agents wrap a model with tools and a control loop so it can take multi-step action — used when the task involves decisions and side-effects, not just generation.
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
A paid 1–2 week engagement to scope a project with rigor before committing to a full build. Outputs: a written scope doc with success metrics, a technical architecture, a 1-week throwaway prototype that proves the riskiest assumption, and a fixed-price quote for the build. Typical price: $8,000–$18,000. Customers who run a discovery sprint with us are 3× more likely to ship on time and budget than customers who skip it.
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.