AI integration for business software that people can trust

Classify Education serves UAE international schools with dynamic data dashboards, AI-assisted analysis and report-comment writing, a Wonde MIS integration for quick student and staff onboarding, plus an EYFS module and parent dashboards. GraphLogic is an enterprise AI platform with a context graph, a reasoning layer where each recommendation traces to its evidence, and governed agents working under budget limits, human approval gates and a full audit trail. Ya-Hub also weaves AI-assisted workflows through its agency modules. The lesson from all three: the surrounding product matters as much as the model. Permissions, data connections, review screens and logs decide whether staff use the feature, so they get as much design attention as the AI itself.

Who this is for

• Software owners who want assisted features inside an existing Laravel or React product
• Education, services and operations businesses in the UAE where staff lose hours to repetitive write-ups
• Teams that have tried a standalone AI tool and found it disconnected from their real data
• Leaders who need AI outputs that can be explained, reviewed and audited

What we build

• Assisted drafting of reports, comments, summaries and replies, always editable by a person
• Analysis of records already in your system, surfaced as insights inside existing dashboards
• Connectors that bring data from sources such as databases, files and business tools into one place
• Approval gates, budget limits and usage logs around any automated action
• Traceable outputs, where a recommendation links back to the records it came from
• Admin controls for turning features on per user, school, team or tenant

How the work runs

1. Pick the taskWe shortlist where AI could help, then test the best candidate on real examples from your data before committing to a build. Some ideas do not survive that test, which is useful to learn early.
2. Connect the dataWe decide exactly what the model may see, strip what it does not need, and build the integration through your existing backend rather than around it.
3. Add human checksOutputs go to a reviewer before they reach a customer, a parent or a record. Where automation is allowed, we add limits, approvals and a log.
4. Review and tuneWe watch real use, collect corrections, and adjust prompts, retrieval and controls. Quality is measured against your examples, not against a demo.

Tech stack

Pricing model

AI features are best added to a product that already works, so we typically scope them as a small fixed-scope project: one defined workflow, an agreed price and date, and source code handed over. If AI is part of a new product, it sits inside the usual focused build, which is typically an 8-14 week fixed-scope MVP for API, web portal and a mobile app. Ongoing tuning, monitoring and new use cases then move to a build and run retainer.

Cost depends on the number of workflows, how many data sources must be connected, the clean-up your data needs, the level of review and approval you require, and the volume of model usage you expect. Model usage is billed by the model provider and is a running cost separate from our build fee, so we estimate it with you before launch. Sensitive data needs particular care. Confirm current data protection requirements for your sector and region, including UAE rules, with your adviser, and tell us early so the design can match. A free scoping call produces a written estimate.

Case studies

Questions buyers ask