AI consulting services anchored on a fixed-fee one-week audit. Day 5, you get a written roadmap with named workflows, walk-away conditions, and a model recommendation grounded in your eval set — not slides. Model-agnostic, operator-led, and openly benchmarked against Big-Four firms, freelancers, and in-house builds. Most teams start with the $3K audit.
Our AI consulting firm runs the same engagement model as our build practice — start with the audit, ladder into delivery only if the data says go. Most teams start with the $3K audit; about a third move into a pilot inside the quarter, and the second workflow is always cheaper than the first.
One week. $3K fixed-fee. Four stakeholder interviews, a data-readiness pass on the systems you care about, eight to twelve workflows plotted on the effort × value matrix, and a written 90-day roadmap on Friday. You leave with the ranked list — you can build with us, build in-house, or hire someone else. Most teams start here.
If the audit surfaces a workflow you want shipped, we move into a fixed-price pilot — four to eight weeks, $10–25K depending on scope. AI implementation consulting wired to your real systems, with a kill point at week two if the metric won't move. The audit's go / no-go is the contract baseline.
Once you have one workflow live, the second is cheaper than the first. From $5K/month, we run a quarterly strategy refresh, monthly drift + cost-of-ownership reporting, and embedded delivery on the next two or three workflows on your roadmap. Cancel any month.
An AI consulting company is judged on the artefact it leaves behind. These are the six workstreams the audit covers, every time — AI readiness assessment, use-case prioritization, model selection, governance, roadmap, and maturity benchmark. None of them get separated into 'phase 2.'
Data, talent, tooling, governance. We score the systems you'd actually pipe AI into — CRM, support tooling, ERP, BI — against a four-dimension readiness rubric. Honest scoring; a 3/5 means "workable with caveats," not "we'll fix it later."
Effort versus value on the workflows you raised in discovery, plus the ones we add. Each use case gets a data-readiness call, a time-to-pilot range, a cost band, and an explicit walk-away condition. No "phase 2" hand-waving.
Claude, GPT, Gemini, open-weights, or a hybrid. We benchmark on your eval set, not the marketing chart. AI implementation consulting where the vendor recommendation comes with the token-cost math — and the reason a cheaper model would have worked.
Where generative AI fits in your business, where it doesn't, and the guardrails your security, legal, and compliance teams will ask for. DPIA template, prompt-injection threat model, audit-logging architecture — written, not gestured at.
Written 90-day roadmap with three to five named workflows, ranked, with realistic time-to-value and total-cost-of-ownership numbers. The artefact your CFO and your steering committee will actually read.
Where you are on a five-tier maturity model versus comparable mid-market peers. Not a vanity score — a delta list that maps to the roadmap and tells you the order in which to fix things.
The $3K audit is not a discovery deck. It is a five-stage pipeline with a named output every day, a confidence score we share with you on Friday, and a written go / no-go on every workflow we surfaced. Run end-to-end in one calendar week.
The audit's primary deliverable is a written 90-day roadmap with three to five workflows, sequenced, with kill-points and cutover dates. Here's a sample — your workflows replace ours, the dates anchor to your kickoff week.
Weeks 1–8
→ continuous · $5K/mo from week 9
Weeks 3–10
→ continuous · $5K/mo from week 11
Weeks 5–12
Weeks 9–12
Sample 90-day roadmap. Every audit produces one of these — your exact workflows, dates, and milestones replace these placeholders.
Every workflow we surface in discovery is plotted here on the third day of the audit. The matrix is the artefact you take into your steering meeting — not a generic 2×2, but eight patterns we have already shipped, scored against your data and your stack. Tap a dot for the audit findings we would write on day 4.
Scoring is per-engagement. Anonymized averages shown. Your matrix on Friday will have your workflow names + your readiness deltas.
Four real options for an AI consulting engagement. BCG, McKinsey, Accenture, and Deloitte run the partner-led playbook; freelancers from Upwork or Toptal cover narrow technical questions; an in-house head of AI is the long-game answer; and our audit-led model sits in between. None of these are wrong — they are different shapes of the same need. Here is when each one wins.
Big-Four pricing reflects public partner-led-engagement ranges in the AI strategy practice. Freelancer ranges based on Upwork + Toptal vetted-rate medians for senior AI consultants. In-house compensation reflects mid-market US head-of-AI salary bands.
Twenty-minute fit call. We will tell you when your problem is squarely in the Big-Four lane or when a freelancer would do the job in two weeks for less. No deck, no obligation.
The audit is not a stand-alone slideware engagement — it is the first phase of the same pipeline that ships the pilot and runs the continuous partnership. Same team, same kill conditions, same operator depth. AI implementation consulting ladders out of the audit's roadmap; nothing else.
$3K fixed-fee. Five-day pipeline from stakeholder discovery through to a written roadmap. Goal: a defensible decision artefact you can take into your steering meeting.
If the audit surfaces a workflow worth shipping, we scope the pilot together. Eval-set design, model selection, integration boundary, kill conditions for week 2. Fixed-price quote before any build kicks off.
Four to eight weeks of build against your real systems. Eval-tested before launch, shadow-mode against your current process, deployed behind a feature flag. Walk-away point at week 2 of the build if the metric won't move.
Continuous AI consulting on the next two or three workflows. Monthly cost-of-ownership and drift reporting. Quarterly maturity-assessment refresh so the roadmap stays current as the model landscape shifts.
Most engagements that go past the audit ship a pilot. Some get killed at day 5 — that's a feature, not a bug. Here's the honest decision graph from the moment you book a discovery call.
Animated graph fires one step at a time. Day-5 verdict is the explicit branch point — kill, defer, or ship.
Same pricing as our other AI services pillars. The audit is the entry point; about 60% of teams move into a pilot inside the following quarter, and most pilot clients stay on a continuous partnership for the next two or three workflows.
Five-day, fixed-fee audit that ends with a written roadmap — not a slide deck.
Ship the top use case the audit surfaced. Fixed price, kill point at week two.
Embedded consulting + delivery on the next workflows on your roadmap.
After the audit, workflows route to the specialty team best suited to ship them — sometimes that's us, sometimes it's your in-house team, sometimes it's a sibling team in our practice. The audit produces the routing decision; we are honest about which team should ship each workflow.
Routing decisions live in the day-5 roadmap. Most audits route 1–2 workflows to a sibling team and 1 back to the in-house team.
Anonymized capability patterns drawn from real audits. The point of each one is what we recommended — including the engagements where the day-5 answer was 'don't ship that workflow.' Named references shared under NDA once we know what you are evaluating.
Inbound team had built a GPT-3.5 lead-qualification agent that worked in demo and stalled in production. CFO had paused the budget; head of revenue wanted it salvaged. No one had written down what 'works' meant.
We ran the 1-week audit. Discovery showed the agent had no eval set and three competing definitions of a qualified lead. Day 3 matrix put lead-scoring at low-effort / high-value. Day 5 roadmap recommended a Sonnet-based shadow pilot against the existing rules engine, with an 8% lift threshold as the walk-away.
Board had mandated 'a generative AI strategy by Q3.' Ops director was wary of vendor pitches. Support team had been pitched three competing GenAI tools and didn't know how to evaluate any of them.
The audit surfaced 11 candidate workflows and killed 6 at day 3 (poor data readiness, or better solved with non-AI tooling). Roadmap picked two: document extraction for proof-of-delivery scans, and an internal-ops RAG agent over the dispatch handbook. Both shipped under the $25K pilot ceiling.
Customer-service VP wanted a voice agent in production by end of year. Audit was scoped to confirm feasibility, model selection, and compliance posture before any build started.
Audit surfaced voice-agent as high-value but high-effort, with two unresolved blockers: call-recording retention required legal sign-off, and latency targets implied a hosting choice the security team hadn't reviewed. Day 5 roadmap deferred the voice-agent pilot by one quarter and shipped compliance doc review first as the lower-risk pattern. VP signed off — preferred a delayed, defensible roll-out to a fast, risky one.
An AI consulting company helps you decide what AI to build, what not to build, and in what order — before you commit a build budget. For us specifically, that means a five-day audit that ends with a ranked use-case roadmap, a per-workflow data-readiness call, a model recommendation grounded in your eval set, and a written go / no-go per workflow. AI consulting services that stop at the deck are the cheap version of this; the audit is the artefact that pays for itself by killing the two or three workflows you would otherwise have shipped and regretted.
Three overlapping things often get bundled. An AI strategy consultant is upstream — they help your executive team decide whether AI is the right answer for the business problem at all, and how to position it in the company's roadmap. A generative AI consulting engagement is narrower — generative AI specifically, with prompt design, vendor choice, and rollout risk in scope. An AI consulting firm like ours covers both, but anchors on a fixed-fee one-week audit so the strategy phase always ends with an actionable artefact. If a firm cannot tell you which named workflows they would ship first and which they would kill, they are doing the slide-deck version of AI consulting, not the audit-led version.
An AI readiness assessment scores four dimensions: data (is it accessible, labeled, current?), talent (do you have the operator depth to maintain a model?), tooling (does your stack support AI workflows without a six-month replatform?), and governance (do you have the security, privacy, and audit-logging posture an AI rollout needs?). You need one if (a) you have never shipped an AI workflow to production, or (b) you have shipped two or three but they keep stalling. Our AI maturity assessment maps you against the five-tier model and the roadmap is sequenced to fix the lowest-scoring dimension first.
Our AI audit is $3K fixed, one calendar week, with a written deliverable on Friday. AI implementation consulting on a single workflow runs $10–25K depending on integration complexity, four to eight weeks, with a walk-away clause at week two if the metric will not move. Continuous AI strategy partnership starts at $5K per month, no annual commitment, and includes a quarterly maturity-assessment refresh. The Big-Four AI practices typically run $150K–$500K for the equivalent strategy phase. We do not bill by the deck; you pay for the artefact, not the partner's billable hours.
We respect that work — the Big Four ship serious AI strategy engagements and several of our clients have hired them in parallel for the board-level narrative. The difference is positioning, not quality. They are best when the AI question is wrapped inside a wider transformation programme, the steering committee needs partner-led validation, and the budget for the strategy phase is in the high six figures. We are best when you have already committed to building AI and want a fast, defensible answer to "which workflows, in what order, on what stack, with what kill conditions?" Many teams use both — Big-Four for the board narrative, us for the operator-level roadmap and the pilot. We will tell you when your problem is squarely in their lane.
Sometimes — and we will say so during the audit if it fits. A freelance AI consultant is usually the right answer when (a) you have a single, narrow technical question ("is our vector DB the bottleneck?"), (b) the workflow is well-scoped, and (c) you have the in-house product depth to manage the engagement. They tend to underdeliver when the question is "where should we start?" because a single individual rarely has the cross-functional vantage point an audit needs. We routinely refer narrow, vetted technical questions to specialists who do not compete with the audit — that is part of the model-agnostic posture.
Eventually, yes — and we will tell you when the maths flips. An in-house head of AI usually pays back once you have three or more live AI workflows and need someone who owns the roadmap full-time. Before that, you are paying $80K–$180K plus equity for someone whose first three months will look a lot like our $3K audit. Several of our continuous-engagement clients have hired in-house leads after twelve months and kept us on a reduced retainer for strategy refreshes — that is the healthy outcome, not a failure.
The Friday deliverable is a written PDF, not a slide deck. It has six sections: (1) workflow map with current-state stack diagram, (2) data-readiness scorecard per system, (3) the eight-to-twelve use case effort × value matrix with our scoring rationale, (4) the 90-day roadmap with the three workflows we recommend shipping first, (5) the per-workflow walk-away conditions, and (6) a pilot scope doc for the workflow at the top of the roadmap, ready to become a statement of work. Sample audits are available under NDA on request — we redact client specifics but keep the structure intact so your team can see exactly what you would receive.
Book the $3K AI audit. Five days, four to six stakeholder interviews, data-readiness scoring on the systems you care about, eight to twelve workflows plotted on the effort × value matrix, and a written 90-day roadmap on Friday. No deck. No obligation to build with us.
AI consulting often ladders into a Claude or OpenAI build, into integration work, or into wider AI automation. These pages go deeper on each lane the audit might land on.
Anthropic specialists for production agents, 200K context, and Claude Code.
Read more 02GPT-4, GPT-4o, Realtime, Codex — and when not to default to OpenAI.
Read more 03Plug AI into Salesforce, NetSuite, Slack — the cross-cluster build piece.
Read more 04Workflow automation: where the audit roadmap lands once shipped.
Read more 05Full-stack AI build company for teams past the strategy phase.
Read more 06Multi-step autonomous agents with LangGraph + tool use.
Read more 07Hire healthcare AI engineers — ambient scribes, prior-auth, EHR integration.
Read more 08Manufacturing AI audit + roadmap — Purdue-stack autonomy gates before any plant integration.
Read more 09Legal AI audit + roadmap — privilege-ring mapping before any build.
Read more 10Travel AI audit + roadmap — model-endpoint approval matrix for PNR / GDPR scope before any inference.
Read more 11Education AI audit + roadmap — FERPA + IDEA scope, integrity-zone mapping, and LMS / SIS gate-in before any inference.
Read more 12HR AI audit + roadmap — EEOC / AEDT / ADA regulatory ledger, bias-audit harness scoping, and HRIS / ATS integration gate-in before any inference.
Read more 13Insurance AI audit + roadmap — claim-lifecycle state machine, underwriting capacity sankey, and fraud-network mapping before any core-system integration.
Read more 14Fintech AI audit + roadmap — risk-score gauges, payment-rails routing, KYC tier-ladder, model-risk-management before any production inference.
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