Your AI engineering partner.
Agentic systems, retrieval, governance and the data foundations that get AI production-ready, built and run by the same team, with evaluation and cost control from week one.
- Copilot, agent or automation
- Inside your app
- In front of your users
- Evaluation set wired into CI
- Retrieval tuned on your corpus
- Guardrails and human approval steps
- Tracing and token accounting
- Model routing and caching
- Risk register and audit trail
- Your VPC or data centre
- Governed data pipelines
- ERP, CRM and legacy systems
- Open-weight or frontier models
Why choose Covaratech for AI engineering.
One team, start to finish
One team owns your system from architecture to on-call. There is no handover wall to throw requirements over.
Evidence before launch
AI features get an evaluation set before they get a launch date. If we cannot measure it, we say so.
Built to be handed over
Documentation and knowledge transfer are contract terms, not favours. You should be able to leave us at any point.
Senior engineers, not a bench
The people who scope your engagement are the ones who build and run it, never handed off to someone you haven't met.
Most AI work fails for reasons that have nothing to do with the model: data that was never made ready for it, retrieval quality nobody tuned, a cost curve nobody modelled, or a pilot with no named owner for the risk once it is live.
We build both sides of that problem as one practice. The evaluation set comes before the feature, so accuracy is a number you can track from week one; the data pipelines, governance and integrations come before the rollout, so a system that works in a demo is still standing a year later with tracing, cost reporting and an auditor's trail behind it.
- Feasibility assessment and prioritised roadmap on your own data
- Inference and GPU serving layer sized to a cost-per-request budget
- Graded evaluation set wired into CI
- Production pipeline with tracing, cost reporting and a rollback runbook
- Governed data pipelines and enterprise system integration
- Risk register and technical documentation an auditor can read
- MLOps and LLMOps pipeline, plus the training to run it without us
What we do.
Pick the one that matches what is blocking you. Most engagements start with a single line on this list.
Agentic AI systems
Agents that call your tools and APIs, with guardrails, human approval steps and a trace behind every action.
AI automation
Document, support and back-office workflows automated end to end, including the extraction, classification and routing that back-office teams still do by hand, with a measured hand-off to a person when confidence drops.
AI product development
Copilots, assistants and AI features designed and shipped as part of your product rather than bolted onto the side of it.
Retrieval and RAG
Chunking, embeddings, hybrid search and reranking tuned against your own corpus until retrieval stops being the bottleneck.
AI evaluation
A graded evaluation set wired into CI, so a prompt or model change is a number you can defend rather than a demo that felt better.
AI observability and cost optimisation
Tracing, token accounting and quality monitoring on every production call, with model routing, caching and context sizing tuned against a cost-per-request budget you set.
AI infrastructure
The compute and serving layer underneath the model: GPU capacity and scheduling, inference servers, model gateways and routing, vector and feature stores, with the autoscaling and cost ceilings that stop a launch becoming an unbounded bill.
On-premise and private AI
Open-weight models running inside your VPC or data centre, for teams whose data is not allowed to leave the building.
AI strategy, roadmap and governance
A prioritised list of where AI actually pays in your business, with the cases that do not clearly marked as such, plus the risk classification, documentation and audit-ready logging the EU AI Act and your sector's rules already require.
Data readiness and enterprise integration
Pipelines, quality, lineage and access control on the data your models depend on, wired into the ERP, CRM and legacy systems that hold it through APIs rather than surgery on systems nobody wants to touch.
Custom models, fine-tuning and MLOps
Fine-tuned open-weight and frontier models for the cases where prompting is not enough, deployed through a pipeline that monitors and rolls them back, so shipping the second model costs a fraction of what the first one did.
Need help with AI engineering?
Thirty minutes with our experts, the people who would do the work. We tell you on that call whether we are the right fit.
Book a call