Enterprise AI in Production

AI doesn’t need to replace your architecture. It needs to work inside it.

We help you integrate AI decision-making into the systems, data and workflows you already run, and we prove it works on your own data before it goes into production.

• Accuracy
• Cost
• Performance
• Integration

Four questions to answer before AI goes into production

Getting a good answer from AI in a demo is easy. Before it goes into a production workflow, you need answers to four harder questions:

Don’t take AI performance on faith. Test it on your data.

Every organisation has different data, different business rules and a different tolerance for error. A vendor benchmark or a polished demo tells you little about how a model will behave on your workload.

Before AI goes into a production workflow, we take representative historical data, define the decisions that need to be made, and benchmark the model against your existing outcomes.

Measure before you commit.

Accuracy: automate what the data supports

Most AI decisions are treated as simply right or wrong. The models we use for decision workflows return a probability or confidence value with every answer, so the workflow can respond to how certain the model is:

The objective isn’t to automate everything. It’s to automate what the data supports.

A confidence score doesn’t make a decision safe on its own. The benchmark tells you what share of your workload could potentially be automated at a defined confidence threshold, and where human review should remain. You then set each threshold according to what an error would cost: a wrong ticket route is not the same as a wrong credit decision.

Cost: measure the economics at your actual volume

The real question isn’t what the AI model costs. It’s the cost per decision, and what that does to the cost of running the workflow. If AI can handle a large share of a workload that currently needs manual review, the comparison is:

With decision-oriented models, the AI processing line can be very small. Jev, for example, is priced per input token, with output free:

That shifts the conversation away from the price per token and towards the economic impact on your operation, which the benchmark measures.

Illustrative figure: 10M × 650 tokens = 6.5 billion input tokens × $0.042 per million. Real token counts depend on your data and questions. Pricing per TypeSafe’s published model page, which is subject to change.

What we’ll measure

Performance: AI that operates inside the workflow

If a model takes seconds to answer, AI ends up as a slow batch step at the end of a process. Decision-oriented models are fast enough to sit inside an API call, a form submission or a pipeline stage:

Published figures are a starting point. The benchmark measures latency on your data, in your environment.

Add AI to your architecture. Don’t rebuild it.

Your existing systems are valuable assets. AI should work with them, not force you to replace them. The AI decision becomes one more step in a workflow you already run:

We integrate AI into existing:

• Databases
• APIs
• ETL pipelines
• CRM
• ERP
• AWS
• SaaS platforms
• Custom applications
• Existing AI / LLM workflows

From AI experiment to production in five steps

1. 01

   Identify

   Find a workflow where AI could create measurable value.

2. 02

   Benchmark

   Run representative historical data through the proposed AI workflow.

3. 03

   Measure

   Evaluate accuracy, confidence thresholds, automation potential, cost and latency.

4. 04

   Integrate

   Connect the AI decision layer to your existing data and business systems.

5. 05

   Deploy

   Move into production with monitoring, thresholds and human escalation where appropriate.

Why IT-ISS

AI expertise is only part of the problem. Production AI has to work with databases, APIs, cloud infrastructure, security, existing applications and business processes.

IT Integrated Business Solutions has been designing, tuning and operating those systems since 2006, from enterprise databases across Oracle, SQL Server, PostgreSQL and MySQL to AWS cloud architecture and custom software. We aren’t an AI consultancy that has learned to call an AI API. We understand the systems AI has to live inside.

One example: TypeSafe’s Jev

We select the technology based on the problem. Jev, from TypeSafe, is particularly interesting for high-volume, structured decision workflows. It is designed for machine-to-machine decisions rather than conversational responses: it classifies, scores, routes and filters data, and returns a typed answer with a probability or confidence value that your code can act on directly.

For a CTO, that means decisions that can be measured, thresholded and audited, at a cost and speed that make AI viable in high-volume workflows.

Frequently asked questions