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.
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.