AI implementation
The demo took a weekend. Production is taking a year. The gap is almost never the model, it is retrieval quality, evaluation, latency, cost per request and what happens when the thing is confidently wrong in front of a customer.
- Common triggers
- A prototype that cannot get a security or reliability sign-off
- Typical shape
- Fixed-scope assessment, then an embedded pod
- Team size
- One to four engineers
- Duration
- Six weeks to nine months
- Roles involved
- GenAI, agentic AI, ML, AI infrastructure, data engineers
CloudOps Network staffs AI implementation work with engineers who have shipped generative and agentic systems into production. Engagements typically cover retrieval design, agent orchestration, evaluation harnesses, observability, guardrails and cost per request, rather than model training.
What the engineers actually do
Retrieval that holds up
Chunking, embedding choice, hybrid search, reranking and the unglamorous data cleaning that decides whether any of it works.
Agent orchestration
Tool boundaries, state, retries, human handoff and a trace you can read when something goes sideways.
Evaluation harnesses
A regression suite for behaviour, so a prompt change does not quietly break last month's fix.
Guardrails and safety
Input and output policy, injection resistance, PII handling, and a documented escalation path.
Cost and latency engineering
Model routing, caching, batching and context discipline. Cost per request is a design constraint, not a monthly surprise.
Production operations
Tracing, dashboards, alerting on quality drift, and a runbook your on-call can actually follow.
Signs you need outside help
AI projects stall in recognisable ways. If two of these are true, the problem is engineering capacity rather than model choice.
It works in the demo and fails on real documents
A retrieval and data quality problem, almost every time.
Nobody can say whether last week's change made it better
No evaluation harness. This is the most common missing piece.
Security will not sign off and nobody knows what would satisfy them
Usually solved by making the system observable rather than by adding a guardrail.
Cost per request makes the business case negative
Almost always fixable through routing, caching and context discipline.
How an AI engagement runs
Assessment
Two to four weeks, fixed price. Where the current system breaks, what production readiness requires, and what it will cost to run at your volume.
Evaluation first
Before changing anything, build the harness that tells you whether a change helped. Everything after this is measurable.
Rebuild the weak layer
Usually retrieval or orchestration. Occasionally the data pipeline underneath. Rarely the model.
Production hardening
Tracing, guardrails, cost controls, load behaviour, failure modes and the runbook.
Handover
Your engineers run it, extend it and understand the evaluation suite well enough to trust it.
Questions on this work
Do you fine-tune or train models?
Occasionally, when there is a clear case for it. Most production problems are solved in retrieval, orchestration and evaluation rather than in model weights, and we will tell you when fine-tuning is the wrong answer.
Which stacks do your engineers work in?
Commonly Python with the major model APIs, vector and hybrid search, orchestration frameworks, and the AWS, Azure or Google Cloud AI services. Tell us your stack and we filter on demonstrated experience.
Can you work with a model we host ourselves?
Yes. Self-hosted and open-weight deployments are a regular part of the work, including the GPU capacity planning that comes with them.
What about data privacy?
Data boundaries are designed at the start, not retrofitted. Engineers work under NDA, and a data processing agreement is part of standard contracting.
Tell us what you are building
Describe the work in a few lines. We come back with a shortlist of engineers who have done it before, their availability, and a rate range. No account, no platform fee, no obligation to hire.
Most briefs get a shortlist within three business days.