AI & Agentic Systems Services

An agentic pilot that impresses in a demo and a system your team can put its name behind in production are different builds. Iseyon does the second one: taking the pilot's intent, wiring it to your governed data, and adding the checks, permissions and audit trail a real decision needs, before handing the running system to your team.

The Journey From Pilot to Production

Every engagement moves through the same sequence, whether the starting point is a proof of concept, a single working agent, or nothing built yet.

Pilot → Evaluation → Governance → Production → Observability → Handover

• Pilot. What exists today: a demo, a notebook, or one working example on a narrow slice of data.
• Evaluation. Test cases, quality thresholds and regression checks defined before anything is rebuilt, so "better" and "worse" have a definition.
• Governance. Permissions, human review points and data lineage agreed with your security and compliance stakeholders, not added after a finding.
• Production. The architecture, integrations and retrieval layer built against your governed data rather than a demo dataset.
• Observability. Logging, tracing, and cost and latency visibility, so a wrong or slow answer is diagnosable rather than a black box.
• Handover. Documentation, runbooks and enablement, so the system is your team's to run and extend.

How We Take an Initiative to Production

1. Assess

Iseyon reviews the existing pilot, its data sources and architecture, and where it would break under real usage: what happens on a question outside its examples, who can currently see its output, and what happens when the model or a dependency changes.

2. Evaluate

Iseyon defines the test cases, quality thresholds and regression checks the system has to pass before and after every change, so an update to a prompt or a model is measured rather than shipped on faith.

3. Govern

Iseyon sets the permissions, the human approval points, and the audit trail the decision requires, agreed with your security and compliance stakeholders during the build rather than after an incident.

4. Build

Iseyon implements the production architecture: the retrieval and grounding layer against your governed data, the integrations the system needs, and routing between models where the workload calls for more than one.

5. Operate

Iseyon adds the monitoring, the cost and latency visibility, and the incident response path an operations team needs to run the system day to day, scoped to what the engagement calls for.

6. Handover

Iseyon hands over the code, the architecture documentation and the reasoning behind it, runbooks for the failure modes seen during the build, and enablement for the engineers who will extend the system next.

Production Concerns We Design For

Not every engagement touches every item below. Which ones apply, and how far each is built out, is set at the assessment stage against the decision the system supports and what happens if it gets that decision wrong.

• Evaluation and regression testing. Defined before a change ships, not inferred after a complaint.
• Grounding and retrieval. Answers traced back to your governed data instead of the model's own memory.
• Human approval gates. Placed on the decisions that need a person's sign-off before they take effect.
• Identity and permissions. A system inherits the access controls of the person or process using it, not a shared key.
• Agent observability. Logging and tracing, so a wrong or slow answer can be traced back to the step that produced it.
• Audit trails. A record of what the system did and why, for the decisions that need one.
• Model routing and fallback. A request handled by the model suited to it, with a defined path for when that model is unavailable or under a quality threshold.
• Cost and latency controls. Budgets and limits set during the build, not discovered on the first invoice.
• Data lineage. A traceable path from the source system to the answer the system gave.
• Incident response. A defined process for what happens when the system gets something wrong in production.

AI Pilot Compared With a Governed Production System

Structural differences between the two, without reference to any particular workload.

What You Receive

• An architecture and production-readiness assessment of the existing pilot
• An evaluation framework: test cases, thresholds and the regression checks that run on every change
• Governance controls: permissions, approval gates and the audit trail the decision requires
• The production build: integrations, the retrieval layer and model routing
• Monitoring and observability, scoped to what the engagement calls for
• Documentation, runbooks and knowledge transfer, so your team owns what ships

A Staged Approach, Not a Fixed Timeline

Every engagement starts with its own assessment, so the phases below describe a shape rather than a fixed-price or fixed-duration commitment. Scope and duration for a given engagement are set at discovery, against the pilot's own gaps.

0-30 days: Assess and plan

Review of the existing pilot, its data and architecture, the production gaps it has, and a first draft of the evaluation plan.

31-60 days: Build and govern

The production architecture, integrations and governance controls: permissions, approval gates and the audit trail.

61-90 days: Operate and hand over

Rollout, monitoring, documentation and the handover to your team.

Ready to see where your pilot stands against a production system? Book an AI architecture review or discuss your AI production roadmap.