AI-Accelerated Delivery
Ship product.
AI in the loop.
Production software delivered with AI as a tool, not as the product. Discovery to handoff in 3-12 months, with governance on the decisions that matter: model risk, cost, data residency, vendor lock-in.
What this is
A delivery engagement, not an AI consultancy.
You get a multidisciplinary team that ships production software, with AI used where it actually compresses the work (and not used where it does not). Governance over model risk, data, cost, and vendor exposure is set up at architecture, not bolted on at release.
- Deliverable is running software. The contract names the production releases, not the activities. You sign for product in your customers' hands, not for prompts in a notebook.
- AI is a tool, not the centerpiece. Code generation, retrieval, structured extraction, agentic workflows when they earn their place. Plain code when AI is overkill. We will tell you which is which.
- Governance from week one. Evaluation harness, prompt library under version control, cost ceilings, data routing rules, model fallbacks. All in place before the first production prompt fires.
- Handoff is part of the deliverable. Successor team inherits running software, the agent stack (skills, context, flows, prompts), an evaluation harness they can re-run, a decision log, and a runbook. No knowledge trapped in a vendor relationship.
This is not a prompt shop and not an "AI strategy" engagement.
Prompt shops sell prompts and dashboards. We sell shipped product. AI strategy decks map use cases. We pick one, build it, and put it in production. Body shops rent you engineers. We staff a delivery team accountable to a contracted outcome.
If your problem is solved by buying ChatGPT seats, we will tell you and not invoice the conversation.
How a delivery runs
Five phases. AI controls applied across all of them.
A standard pilot is 8 weeks, MVP 4 months, Scale up to 12. The shape stays the same: linear delivery with the AI control plane (prompts, eval, supervision, cost) running underneath every phase. No phase ships without its AI controls signed off.
Discovery
- Use-case shortlist scored
- Build-vs-buy call on AI parts
- Data audit & classification
- Success criteria written
Architecture
- Model-agnostic gateway
- Eval harness scaffolded
- Cost ceilings per feature
- Data routing rules signed
Build
- 2-week sprints, governed
- Weekly eval & cost review
- Prompt library versioned
- Human-in-the-loop where needed
Release
- Pre-flight eval threshold
- Hallucination rate measured
- Rollback path rehearsed
- Governance dashboard live
Handoff
- Runbooks & on-call docs
- Eval harness transferred
- Decision log exported
- Two-week shadow with successor team
Prompt library
Versioned, reviewed, tested. Promoted on the same path as code.
Eval harness
Quality & hallucination scored per release. Threshold gates ship.
Human supervision
HITL, structured output, or retrieval-grounded. Chosen per use case.
Cost ceilings
Per-feature budgets enforced in the gateway. Overruns pause, not bill.
Deliverables
What you walk away with.
Production releases
The product, in your customers' hands. Named, scoped, contracted up front. Pilot is one production-grade flow; MVP and Scale ladder up from there.
Governance dashboard
Decisions, costs, AI usage, model-error trends, eval scores. Board-readable, refreshed every steering. The thing your CFO and CISO ask for and never get.
Evaluation harness
Reproducible eval suite tied to the use cases. Hallucination rate, accuracy, latency, cost-per-task measured per release. Your team re-runs it without us.
Handoff documentation
Runbooks, on-call playbook, architecture decision records, model fallback procedures, vendor risk register, EU AI Act posture note. A successor team is productive in week one.
Who calls us for this
Three trigger moments.
The AI-curious CEO
Board is asking what the AI story is. You have a candidate use case but the in-house team is tied up. You want a working pilot in customers' hands within a quarter, not a 60-slide AI strategy.
The bandwidth-constrained scale-up
Engineering team is at capacity on the core product. You have an obvious AI-augmented feature on the roadmap but no one to build it without slipping the other commitments. You want a contracted outside team, not a permanent headcount.
The regulated buyer who needs evidence
Financial services, health, public sector. You can not ship AI without an EU AI Act dossier, a hallucination threshold, and a data-routing posture. You need a partner whose default is to build the evidence with the product, not bolt it on at audit.
Pricing
Three engagement tiers.
Fixed-fee per phase, named scope, named release. Cost ceiling on inference is part of the contract. Scoping call is not billable.
One production-grade flow, in customers' hands, with eval harness and governance dashboard standing up alongside. Right when the use case is identified but the build-vs-buy answer is not.
Pilot through to a full MVP release, multiple use cases, integrated with your product surface. Eval harness becomes part of CI. Governance dashboard goes board-readable. The most common shape.
Multiple AI-augmented features at production scale, regulated-environment posture, in-house team trained alongside, full successor handoff. Right for the buyer where AI is a product pillar, not an experiment.
Inference cost is billed at provider rates, capped by the per-feature ceiling. Governance Audit available as a 3-week prelude when use-case priorities are not yet sorted.
Common questions
Six things buyers ask first.
Who owns the code and the model artifacts?
You do. The master services agreement assigns all source code, the agent stack (skills, context configs, flow definitions, prompt library), evaluation datasets, fine-tunes, and model artifacts to your company on delivery. We retain the right to use generic engineering patterns and tooling we brought in. No code or data we touched stays on our infrastructure after handoff.
What if the LLM provider we picked goes away or doubles its price?
We architect against vendor lock-in by default. Model calls go through an abstraction layer (eg LiteLLM, an internal gateway, or a provider-agnostic SDK) so the underlying model can change without touching product code. Evaluation harness runs against more than one provider during build, and we document a fallback model per use case. Vendor risk is on the risk register from week one.
Does our data leave the perimeter when you run prompts?
Only under terms you sign. We default to providers with zero-retention, no-training contractual commitments (Azure OpenAI, Anthropic via AWS Bedrock, OVHcloud, Scaleway, on-prem). For regulated data we keep the model call inside your VPC. Data classification and routing rules are agreed at the architecture phase and enforced in the gateway. EU AI Act and GDPR posture is documented before the first production call.
How do you keep AI cost from running away?
Per-feature budget ceilings set at architecture, enforced in the gateway. Token and request metrics are wired into the governance dashboard from day one. Build-phase cadence reviews the cost-per-task trend weekly. If a feature exits the budget envelope, it pauses for a redesign call, not an invoice overrun. The contract names the budget explicitly.
How do you handle hallucination liability?
We do not deploy unsupervised model output into a flow where a wrong answer is expensive. Every user-facing AI surface goes through one of three patterns: human-in-the-loop confirmation, structured-output validation with rejection paths, or retrieval-grounded with citation. The evaluation harness measures hallucination rate per release, and the threshold for shipping a use case is named in the contract.
What is the difference between this and buying a prompt-shop engagement?
A prompt shop sells you prompts and walks away. We ship the product. The deliverable is running software, owned by you, with a governance dashboard, an evaluation harness, and a successor brief. AI is one tool we use to get there. If your problem is solved by buying ChatGPT seats, we will tell you and not invoice the conversation.
Ready to scope
One scoping call.
Build-vs-buy answer in 48h.
Email us a few lines about the use case. We reply within 24h, Monday to Friday.
- The use case in one paragraph
- Who the user is and what success looks like
- Data sensitivity and regulated context, if any