AI that survives contact with production
AI & Automation
Most AI initiatives die between the demo and the production system, where accuracy, cost and accountability suddenly matter. This practice exists for that gap. Six AI capabilities under one evaluation-first discipline, from the first ranked use case to models watched in production.
Governed AI, briefly explained
How evaluation-first delivery takes an AI case from idea to watched production.
90 secSample frame · video pending01Six practices, one honest question
Start from where AI actually stands today.
AI vendors love to start from their technology. This map starts from your position. Say where AI sits on your roadmap and the practices that fit rise with a reason attached, whether that is a first ranked use case or a production model nobody currently owns. When two situations apply at once, begin with the one closest to revenue.
There is pressure to act on AI and no agreed place to begin.Consulting turns pressure into a ranked list of cases with numbers attached. Generative AI work then proves the strongest one quickly.
A specific AI feature is scoped and now has to be engineered properly.AI software development carries a case from model choice to production code. Agent work fits when the feature must act, not just answer.
Models are useless until they sit inside the tools your teams already use.Integration work wires models into your CRM, ERP and internal tools with contracts and fallbacks, so adoption does not depend on new habits.
Something is live and nobody is watching cost, drift or quality.Managed AI keeps evaluation, spend and model behaviour under a named owner. Consulting steps in when the strategy itself needs a reset.
AI Consulting
Ranked use cases, honest feasibility and a funded starting point.
AI Software Development
Custom AI development from model selection to production code.
Generative AI
Language-model products with evaluation and guardrails designed in.
AI Agents
Agents that act inside bounded permissions your team defines.
AI Integration
Models wired into the systems your business already runs on.
AI Implementation
One defined use case taken to production by a forward deployed engineer in your stack.
Managed AI Services
Cost, drift and quality watched in production by a named owner.
02Evaluation before enthusiasm
Measured at every step, or not shipped.
AI work fails quietly, which is why this system refuses unmeasured claims. Each engagement moves through the same four gates, and each gate closes with evidence a sceptical stakeholder can inspect. Enthusiasm is free. Evidence is the deliverable. Every gate doubles as a safe exit, so a weak case can die cheaply at any point in the journey.
Find the case worth funding
Workshops rank candidate uses by value, feasibility and risk against your data reality. Weak cases are killed on paper, where killing them is cheap. The survivors arrive costed, with the data work each one needs stated up front.
A case with a number attachedProve it before scaling it
A thin working version runs against your actual data and your quality bar. Accuracy, cost per call and failure modes are measured, not estimated. What the proof learns reshapes the case before serious money moves.
A proof on your real dataShip with control designed in
Human checkpoints, permission boundaries, fallbacks and logging are engineered into the product. Enterprise ai development services earn trust here or nowhere, and the control design is documented for your risk owners.
Guardrails an auditor acceptsOperate it like software
Live models get monitoring for drift, spend and quality, plus a review cadence. AI that nobody watches becomes a liability on a schedule, so ownership is assigned before launch rather than after the first incident.
A model with a named owner
03Evidence and proof
AI that is still running a year later.
The proof standard for this practice is production evidence: measured accuracy, measured cost and a client willing to sign the numbers. These previews show that standard while written approvals are collected.
Explore case studiesA support copilot that answered 6 in 10 tickets, safely.
Tern needed deflection without hallucinated refunds. Retrieval over governed content, confidence thresholds and human handoff shipped in one quarter.
- Tickets resolved without escalation
- 6 in 10
- Answer accuracy at launch
- Measured weekly
Document intake automated with an auditable trail.
Koru Health processed clinical intake forms by hand. Extraction models with human verification checkpoints cut the queue while keeping every decision reviewable.
- Manual intake effort
- Cut by two thirds
- Decisions with audit trail
- All of them
Every vendor showed us demos. This team showed us our own error rate, then made it fall for six straight weeks.
Sample · pending client approval
04Three ways to engage the practice
Match the door to your risk appetite.
Some teams want a decision, some want a product carried, some want AI engineers inside their own process. All three doors lead to the same delivery discipline, so the choice is about who steers, never about quality. The smallest door is a scoping engagement that prices the decision before anyone commits to a build. The doors also combine well, and many clients scope first before commissioning anything.
- Project Scoping
Scope the AI decision
A short priced engagement that turns AI pressure into a ranked case list, an architecture and a number the board can approve or refuse.
- AI Software Development
Commission the build
One accountable team takes a proven case to production, with evaluation, guardrails and operations included rather than sold separately later.
- Hire AI Engineers
Add AI engineers to your team
Engineers who have shipped models join your reviews and standards, bringing the discipline without replacing your process.
05Straight answers
Asked before AI budgets get signed.
01How do we know if our use case is realistic?
Bring it to a scoping conversation and it gets tested against three things: the value if it works, the state of your data, and the tolerance for error in that workflow. Most cases fail on the second or third test, which is exactly why testing them on paper first is the cheapest step in the whole journey.
02Do you build on OpenAI, open-source models or our own?
Whichever the case justifies. Hosted frontier models win when quality matters most and data terms allow them. Open-source or fine-tuned models win on cost, latency or data residency. The choice is recorded with its trade-offs, so you can revisit it as the model market moves.
03How is hallucination risk actually handled?
Through architecture rather than hope. Retrieval grounds answers in your approved content, confidence thresholds route weak answers to humans, and evaluation suites measure failure modes before and after every change. Where the workflow cannot tolerate error, the design keeps a person in the loop by construction.
04What does AI development cost?
A scoping engagement is fixed-price and small. Build work is priced against the proven case, and running costs are modelled per call before you commit, because inference spend is a real line item. An ai development company that will not discuss unit economics is selling you a demo.
05Can you work with our compliance and security constraints?
Constraints are collected before architecture, not discovered after it. Data residency, retention, access control and audit requirements shape model choice and hosting from the first diagram. Regulated buyers get the evidence pack their auditors ask for as part of delivery.
06We already have data scientists. Where do you fit?
Usually on the engineering side of the divide. Your scientists know the models, and the gap tends to be production software, integration and operations. As an ai solutions company we ship the system around the model, and your team keeps the science.
07What is the difference between AI consulting and AI development here?
Consulting decides what deserves to exist and proves it on paper: ranked cases, feasibility against your data, and a costed recommendation. Development engineers the chosen case into production software. They are staffed as one practice, so nothing gets lost in the handoff between the deck and the codebase, and the consultants stay accountable for what the builders inherit.
08How fast can a first AI project deliver?
A ranked case list arrives in weeks. A working proof on your data typically lands within the first quarter, and it is designed to grow into the product rather than be thrown away. Speed comes from starting narrow, never from skipping evaluation.