We do not “add AI”. We engineer systems that reason.
Aitina Tech's AI practice builds agentic systems that survive production: grounded in a real knowledge layer, measured against an evaluation set, fenced by guardrails, and running inside your own cloud account. The research behind it is published in full on The Software Lens.
What you leave with
- The AI use case most likely to pay back, and the ones to avoid
- What it would take to run it safely: data, permissions, evaluation and cost
- A first build small enough to prove the idea in weeks
With a senior architect, not a salesperson. Sixty minutes, no cost, no obligation.
The demo works. Production is a different discipline.
Almost every organisation now has an AI prototype that impressed a room. Very few have one in production carrying real load, real data and real accountability. The gap is not model quality, everyone has access to the same models. The gap is engineering: grounding, evaluation, guardrails, cost control, observability and a clear answer to “why did it do that?” when someone senior asks.
What usually goes wrong
Answers that are fluent and wrong. A retrieval layer nobody owns. Costs that scale with usage in a way no one modelled. No way to prove behaviour has not regressed since last month.
What we do instead
We treat an agent as a production system with a contract: defined inputs, a knowledge layer, an evaluation set, guardrails, a cost model, telemetry and an audit trail. Boring, and it is exactly what makes it shippable.
Where the research comes from
AIM and SaaC are the research of Imtiaz Hassan, published in full on The Software Lens. Our R&D wing applies them, and that is the architecture we build to. You can read the whole thing before you hire anybody.
Read the researchSix things that turn a model into a system.
Agentic systems
Agents that plan, call tools and hand off to each other, with the seams drawn so you can reason about failure. Built on the Agentic Interface Model, our own architecture work.
The AIM architectureKnowledge & retrieval
The three-tier Knowledge Store: structured records for facts, vector retrieval for similarity, a relationship graph for context. Grounding is a data problem before it is a prompt problem.
Read the patternEvaluation & guardrails
Golden sets, regression suites, refusal policy, PII handling and human-in-the-loop gates, so behaviour can be demonstrated to a risk committee, not just to a friendly audience.
Platform on AWS Bedrock
Model access and routing, prompt and version control, cost attribution, tracing and observability: running in your own account, inside your own data boundary.
Tooling & MCP integration
Giving an agent safe, typed access to the systems you already run, over MCP or conventional APIs, with permissions and rate limits that hold under pressure.
Integration workCompound learning layer
Capturing decisions, retrievals and outcomes so the organisation gets sharper each cycle instead of starting over. The edge is no longer the model, it is this.
The OKL frameworkFrom “we should do something with AI” to something that runs.
Short, checkpointed and cancellable. You are never more than a few weeks from the next honest decision point.
Frame
Two to three sessions. What decision or task is actually being automated, what does good look like, what is the cost of being wrong. Half the candidates get eliminated here, which saves you the budget.
Ground
Build the evaluation set and the knowledge layer before the clever part. If we cannot measure the answer, we do not build the agent.
Build
The system itself, in your cloud account, with guardrails, tracing and cost attribution from day one rather than retrofitted.
Operate
Regression runs on every change, drift monitoring, cost review and a documented audit trail. Handover to your team, or we run it.
Runs in your cloud, not ours
AWS Bedrock, Azure OpenAI or Google Vertex AI: inside your own account, your own VPC and your own data residency. We do not hold your data, and there is no platform of ours for you to be locked into. When the engagement ends, you own everything, including the evaluation sets.
Built for the questions auditors ask
Model and prompt versioning, retention and PII policy, human review points, decision logging and an evidence trail. If ISO 27001 or the EU AI Act is on your horizon, the controls belong in the architecture now rather than bolted on later.
Standards & ISO readinessA small, senior team that stays accountable.
- Architect-led. Every engagement is led by a senior architect who stays accountable for the decisions, from the first hour to the handover.
- Over twenty years of building and integrating software. Enterprise integration, cloud platforms, product delivery and now AI agents, across sectors where systems cannot fail.
- Independent and vendor-neutral. AWS, Azure and Google Cloud, and whatever platforms you already run. We recommend what fits, including doing less.
- Research, applied. The patterns we use are published openly on The Software Lens and applied by our R&D Wing, so you can read the reasoning before you hire us.
- We run what we recommend. We build our own product, Nexcubator, on the same architecture we design for clients.
- You own everything. Your repository, your cloud account and your documentation from the first week. Based in Ireland, working across the EU and beyond.
Questions we get asked
Do we need our own data science team first?▼
No. Most of what makes an AI system work is software engineering: retrieval, contracts, evaluation, observability and cost control. We work alongside whatever team you have, and we deliberately hand over the evaluation sets and runbooks so your people can own it.
Which model should we use?▼
That is usually the least important decision, and it should stay reversible. We build behind a routing layer so models can be swapped as prices and capabilities change, which they do, constantly. Locking your architecture to one vendor's model is the mistake to avoid.
Will our data be used to train someone's model?▼
Not in the architectures we build. We run inside your own cloud account using enterprise model endpoints that do not train on your inputs, with data residency set explicitly. That boundary is written into the design, not assumed.
How do you know whether it is actually working?▼
An evaluation set built before the system, run on every change, with the results tracked over time. If a change makes answers worse, you find out in the pipeline rather than from a customer. This is the single biggest difference between a pilot and a product.
What if AI is the wrong answer for our problem?▼
Then we say so, and we would rather say it in the free hour than three months into a budget. A good deal of what gets pitched as AI is a reporting problem, a data-quality problem or a process problem wearing a costume.
One free hour. No pitch, no obligation.
Bring the problem you are stuck on: an integration that keeps breaking, a cloud bill nobody can explain, an AI project that is all demo and no product, or a platform you are about to invest in. You will leave with a straight answer and a written summary, whether or not you ever work with us.
- Architecture and integration review
- AI feasibility: what will actually work, and what will not
- AWS, Azure and Google Cloud cost and design
- Business process audit and ISO readiness
- Technical due diligence before you invest or acquire
Agents that can show their work.
The Act asks every AI system to say what it does, keep a record of what it decided and name who is responsible. We build those three into the agents themselves, so the evidence is produced as they run.
The EU AI Act, in practice