AI native consulting
The strategy layer before you build.
AI native consulting is the assessment, build-vs-buy call, architecture blueprint, and roadmap that comes before a production build — done by the same accountable team that can then deliver it. We're not a marketplace and we don't refer you to a third party and disappear: we assemble and manage a vetted network of senior AI experts ourselves, and stay accountable end-to-end for scope, price, and delivery.
Assessment from $4k · Refundable 7 days · Roadmap in 1-2 weeks
In one sentence
AI native consulting means an AI-readiness assessment, a build-vs-buy decision, an architecture blueprint, and a sequenced roadmap — delivered by a hand-picked network of senior AI experts that we vet and manage directly, with us staying the single point of accountability for scope, price, and delivery if the roadmap turns into a build.
Key facts
- Engagement model
- Assessment, then build — same accountable team
- Assessment (strategic phase)
- $4-7k · 1-2 weeks
- Delivery model
- Vetted, managed network — never a bare referral
- Roadmap horizon
- 12 months, phased and separately priced
- Accountability
- Single point of contact for scope, price, delivery
- Vendor referral fees
- None taken (honest build-vs-buy calls)
Our guarantee
- Production by week 7 or 50% back
- If we miss the production milestone, you get 50% back — written into the SOW.
- 7-day no-risk window
- Cancel within 7 days of signing, no questions asked. No lock-in after.
- Fixed-price, no lock-in
- Phased fixed-price engagement. Run is month-to-month — stop any time.
Senior operators, AI-augmented delivery · NIST AI RMF-aligned governance
What is AI native consulting?
It's the work of figuring out how an organization becomes AI-native, before committing to any single build: where you stand today, whether to build in-house, buy a platform, or work with a managed team like ours, what the target architecture looks like, and in what order to sequence the workflows worth automating. Most firms selling this label stop at the recommendation. We treat the Assessment as the first phase of one engagement — the same accountable team that writes the roadmap can staff and deliver against it, so the strategy doesn't have to survive a handoff to strangers.
What we assess before anyone recommends a build
Six decisions we work through in every Assessment. Each one lands in a roadmap phase that's scoped and fixed-price on its own — not a bundle you have to commit to all at once.
AI-readiness assessment
Where your data, systems, and team actually stand versus what an AI-native operating model requires. We score readiness across data access, workflow structure, governance maturity, and team capacity — no generic maturity-curve slide, a scorecard tied to your systems.
Build-vs-buy decision
In-house team, a managed network like ours, or a SaaS AI platform — the honest answer depends on your team's capacity, time-to-value pressure, and IP sensitivity, not on what we'd prefer to sell you. We document the trade-off and the decision criteria.
Architecture blueprint
Model family (Claude, GPT, Gemini, or multi-LLM routing), retrieval approach (RAG, hybrid search, agentic), and deployment surface (your cloud, Bedrock, Vertex, direct API) — selected against your cost, latency, and governance constraints, not a generic reference architecture.
12-month roadmap and sequencing
Which workflows to tackle first, in what order, and why — sequenced against capital, team bandwidth, and risk. The roadmap is scoped into phases you can commit to individually; nothing requires a single large upfront bet.
Governance and AI RMF design
Your AI workflow mapped against NIST AI RMF (Govern, Map, Measure, Manage): approved sources, prompt versioning, reviewer queues, audit logs, attestation cadence — reviewable by your risk officer before a single line of production code ships.
Network staffing plan
Which specialists your roadmap actually needs — the network is assembled to the engagement, not sold as a fixed bench. You see who's staffed, their vetting record, and the accountable lead before work starts, and you deal with that lead throughout.
A managed network, not a strategy firm or a marketplace
Two other models sell something that looks like AI native consulting. A traditional strategy firm staffs junior consultants you never vetted and hands you a shortlist at the end. A freelancer marketplace connects you to one specialist and steps back the moment the invoice clears. Neither stays accountable once the engagement gets hard.
| Dimension | Traditional strategy firm | Freelancer marketplace | AI-Native Agency |
|---|---|---|---|
| Who does the strategic work | Partners scope it, then hand execution to junior staff you never vetted | You post the brief; a freelancer bids and does the work solo | A hand-picked network of senior AI experts, vetted and managed by us — same team you talk to |
| Accountability if something breaks | Diffuse — the engagement partner is rarely the person who did the work | None past the invoice; you coordinate replacements yourself | We stay the single point of accountability for scope, price, and delivery |
| Handoff to the build | New team, new context, new statement of work — you re-explain everything | There is no build; you're on your own to find and manage one | Same engagement, same accountable party — assessment flows straight into a scoped build |
| Vetting of specialists | Internal staffing you don't see or approve | Rating stars and a profile page | We interview, reference-check, and manage every expert we staff — you never coordinate a stranger alone |
| Pricing model | $80k-$250k strategy-only retainer, hourly overruns common | Hourly, per-freelancer, no ceiling on scope creep | Fixed-price phases: Assessment $4-7k, Build scoped and fixed from there |
| What you're left holding | A report and a vendor shortlist to go execute yourself | A freelancer's deliverable and no one to call if it needs revision | A working roadmap plus, if you continue, the same accountable team shipping it |
How the network model actually works
The detail that separates a managed network from a referral — why we built it this way, and what stays the same for you whether one specialist or five end up on the engagement.
Why "AI native consulting" means something narrower than the phrase suggests. Most firms using this label sell one of two things: a strategy deck with a vendor shortlist at the end, or a staffing marketplace that connects you to a freelancer and steps back. Both leave you doing the hard part alone — coordinating a specialist you didn't vet, managing a handoff between the people who scoped the work and the people who build it, and owning the risk if either one falls through. AI native consulting, as we practice it, is the strategic layer — assessment, build-vs-buy, architecture, roadmap — done by the same accountable team that can then staff and deliver the build. The label describes an outcome (your organization operating AI-natively) rather than a document, and we scope engagements against that outcome, not against a deck.
How the network works, in full. We scope, contract, and deliver the work ourselves — staffed by a hand-picked network of senior AI experts that we vet and manage directly. You're never handed off to a freelancer to coordinate alone; we stay the single point of accountability for the outcome, the price, and the week-7 guarantee. Concretely: when your roadmap calls for a specialist we don't carry full-time — a healthcare-compliance architect, a retrieval-systems specialist, someone who's shipped three production deployments on a specific model family — we staff that person from a network we've already interviewed, reference-checked, and trial-tested. They report through us. You get one point of contact, one contract, one price, and one guarantee, regardless of how many specialists end up touching the engagement. If a network expert underperforms, that's our problem to fix, quietly and on our dime — not a coordination failure that lands back on your desk.
Why we built it this way instead of hiring a large permanent staff or running a pure marketplace. A large fixed bench means you pay for idle capacity between engagements, and the roster rarely matches what any one engagement actually needs. A pure marketplace pushes the vetting, coordination, and accountability risk onto you — exactly the overhead a consulting engagement is supposed to remove. The managed-network model is the middle path: senior depth assembled to the engagement, but with us — not you — carrying the vetting burden and the accountability. We're not a marketplace and we don't refer you to a third party and disappear — we assemble and manage a vetted network of senior AI experts ourselves, and stay accountable end-to-end for scope, price, and delivery. No vendor referral fees, no rating-and-hope profile page, no second contract to negotiate with someone we introduced you to.
From roadmap to build, without the handoff loss. The reason assessment-only engagements underperform is rarely the analysis — it's the seam between "here's your roadmap" and "now go get it built." Every assumption baked into a roadmap has to survive a handoff to people who weren't in the room when it was made. Because the same accountable team runs both phases here, that seam doesn't exist: the architecture decisions in your blueprint are made by the people who will own building against them, the sequencing in your roadmap accounts for the network specialists who are actually available to staff it, and the fixed prices we quote for downstream build phases are grounded in a team that already understands your systems. You can still stop after the Assessment and take it elsewhere — but if you continue, you're not starting over with strangers.
When should I start with AI native consulting vs going straight to a build?
Start here if you don't yet know which workflow to automate first, whether to build in-house or buy, or what a target architecture should look like for your organization broadly. If you already know the workflow and just need it scoped, architected, and shipped, go straight to AI automation consulting — the build-side engagement. Unsure which decision you actually need? Our build-vs-buy guide walks through the criteria we use in every Assessment.
Questions leadership teams ask before starting an assessment
What is AI native consulting?+
AI native consulting is the strategic layer that comes before an AI build: an assessment of how ready your organization is to become AI-native, a build-vs-buy decision, an architecture blueprint, and a sequenced roadmap. Unlike a pure strategy firm, we don't hand the roadmap to someone else to execute — the same engagement can flow straight into a scoped, fixed-price build with the team that wrote the roadmap staying accountable for it.
Are you a marketplace, or do you refer us to a third-party freelancer?+
No. We scope, contract, and deliver the work ourselves — staffed by a hand-picked network of senior AI experts that we vet and manage directly. You're never handed off to a freelancer to coordinate alone; we stay the single point of accountability for the outcome, the price, and the week-7 guarantee. We're not a marketplace and we don't refer you to a third party and disappear — we assemble and manage a vetted network of senior AI experts ourselves, and stay accountable end-to-end for scope, price, and delivery. No vendor referral fees change hands, and no rating-and-hope profile page stands between you and the person doing the work.
How is AI native consulting different from your AI automation consulting?+
They sit at different altitudes of the same engine. AI automation consulting starts from a specific workflow you already suspect should be automated and moves fast into architecture, build, and production. AI native consulting starts one level up — before you've picked a workflow — with the assessment, build-vs-buy call, and 12-month roadmap for becoming an AI-native organization overall. Both end in the same place: a scoped, fixed-price build delivered by the same accountable team. Most engagements that start here narrow into an automation-consulting scope once the roadmap picks the first workflow.
What's actually in an AI native consulting engagement?+
Four deliverables from the Assessment phase: an AI-readiness scorecard against your real systems and data, a build-vs-buy recommendation with the trade-offs documented, an architecture blueprint (model family, retrieval approach, deployment surface), and a 12-month roadmap sequencing which workflows to automate first and why. Each roadmap phase is priced separately so you can commit workflow by workflow instead of one large upfront bet.
How much does AI native consulting cost, and how long does it take?+
Assessment is fixed-price at $4,000-$7,000 for 1-2 weeks — a scorecard, build-vs-buy call, architecture blueprint, and roadmap. If the roadmap points to a build, that phase is scoped and priced separately from the Assessment output, typically $15,000-$40,000 per workflow with production by roughly week 6-10. Assessment is the only commitment to start; nothing downstream is bundled or assumed.
Who actually does the strategic work — your employees or the network?+
Both, working as one team. We lead every engagement and stay the accountable point of contact throughout, and we staff in senior specialists from our vetted network when the roadmap calls for depth we don't need to carry full-time — a healthcare compliance architect, a retrieval specialist, a specific model's fine-tuning expert. You never manage a network expert directly or coordinate a handoff; every specialist reports through us, and we remain responsible for the scope, the price, and the delivery date regardless of who's staffed.
How do you vet the experts in your network?+
Interview plus reference check plus a scoped trial task before anyone joins an active engagement, and we only staff someone we'd be willing to stay accountable for. That last part is the filter that matters: because we don't get to walk away if a network expert underperforms — we own the outcome — we vet harder than a marketplace rating system ever will, and we replace anyone who doesn't meet the bar without you having to notice or manage it.
We already have an internal AI team — do we still need an assessment?+
Often, no — or a lighter version of it. If your team already has a clear build-vs-buy answer and an architecture direction, we'll say so and scope straight into a build SoW instead of billing you for an assessment you don't need. Where it's still useful with an internal team: an outside AI-readiness scorecard for board or budget conversations, or an architecture second opinion before a large build commitment. We'll tell you honestly which applies in a free scoping call before any Assessment is billed.
What happens after the roadmap — are we locked into using you for the build?+
No. The Assessment output — scorecard, build-vs-buy recommendation, architecture blueprint, roadmap — is yours to take in-house, to a different vendor, or back to us. If you continue with us, the build is scoped and priced as its own phase with its own fixed price and its own week-7 production guarantee. Nothing about the Assessment obligates you to the build, and nothing about the network model changes who you deal with if you do continue: still us, still accountable end to end.
Why not just hire a freelance AI consultant directly and skip the markup?+
You can, and for a narrow, well-defined task that's often the right call — we'll tell you so in a scoping call rather than sell you an Assessment you don't need. Where a solo freelancer usually breaks down is coordination and continuity: no one owns the outcome if the freelancer under-delivers or moves on mid-engagement, no one else has the context if a second specialist is needed, and there's no accountable party for price or the delivery date. The network model exists specifically to remove that risk — you get senior specialist depth without becoming the project manager who has to coordinate, vet, and backstop them yourself.
Popular with buyers
Start with the assessment
Ready to start? Assessment is the strategic phase — $4-7k, 1-2 weeks.
Output: an AI-readiness scorecard, a build-vs-buy recommendation, an architecture blueprint, and a 12-month roadmap sequenced into fixed-price phases. The only commitment to start. Continue with us, take it in-house, or stop — your call. If the honest answer is that AI isn't the right investment yet, we say so.