Your Enterprise AI partner.
Strategy, governance, data foundations and the platform work that gets AI past a pilot and into the business.
Most enterprise AI pilots never reach production, and the reason is rarely the model. It is data that was never prepared for it, a legacy system nobody can get records out of, and no named owner for the risk.
This practice is the work either side of the build: deciding what is worth doing, getting the data and the integrations in place, and leaving behind a governance trail that survives an audit and a team that can run the thing.
What you get
- Prioritised roadmap, with the cases that do not pay ruled out
- Risk register and technical documentation an auditor can read
- Governed data pipelines feeding the models
- MLOps pipeline, plus the training to run it without us
What we help with.
AI strategy and roadmap
A prioritised list of where AI actually pays in your business, with the cases that do not clearly marked as such.
AI governance
Risk classification, technical documentation, audit-ready logging and human oversight, built for the EU AI Act and the sector rules that already apply to you.
Data readiness for AI
Pipelines, quality, lineage and access control on the data your models depend on — the reason most pilots stall short of production.
Enterprise system integration
AI wired into the ERP, CRM and legacy systems that hold the data, through APIs rather than surgery on systems nobody wants to touch.
Custom models and fine-tuning
Fine-tuned open-weight and frontier models for the cases where prompting is not enough, with the evaluation to show it was worth the spend.
MLOps and LLMOps
The pipeline that deploys, monitors and rolls back models, so shipping the second one costs a fraction of what the first did.
Document intelligence
Extraction, classification and routing for the contracts, invoices and forms that back-office teams still read by hand.
Rollout and enablement
Training, internal guidelines and the change work that decides whether what you built is actually used once we leave.
Why choose Covaratech for enterprise ai.
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.
Need help with enterprise ai?
Strategy, governance, data foundations and the platform work that gets AI past a pilot and into the business.
Talk to usQuestions about enterprise ai.
What comes up on the first call, with the answers we give on it.
1.How do you decide which AI use cases are worth pursuing?
We build a prioritised roadmap and rule out the cases that do not pay, rather than handing over a long list of ideas. That roadmap is one of the deliverables, not a slide shown once and forgotten.
2.How do you handle AI governance and regulation, like the EU AI Act?
Risk classification, technical documentation, audit-ready logging and human oversight, built for the EU AI Act and the sector rules that already apply to you — with a risk register and documentation an auditor can actually read.
3.Why do most enterprise AI pilots stall before reaching production?
Usually the data underneath was never made ready: pipelines, quality, lineage and access control. Governed data pipelines feeding the models are one of the deliverables, because that is the reason most pilots stall short of production in the first place.
4.Can AI be wired into our existing ERP or CRM without replacing it?
Yes — enterprise system integration goes in through APIs against the ERP, CRM and legacy systems that already hold your data, rather than surgery on systems nobody wants to touch.
5.What happens after a model is deployed?
An MLOps and LLMOps pipeline that deploys, monitors and rolls back models, plus the training to run it without us — so shipping the second model costs a fraction of what the first one did.