Build search, recommendations and document processing into the systems your team uses. We define the data, access rules and acceptance checks before developing the feature.
Discuss your AI use case →AI services in one operational view.
The AI fleet panel brings service availability, processing activity and data freshness into GBA. The team can see what has completed and what needs attention alongside its daily work.
An assistant can turn a business question into a database query. We define the metric, limit the permitted data and validate the query before execution. The interface below shows the steps to review.
SELECT p.name, SUM(o.order_value) AS total
FROM products p
JOIN orders o ON p.id = o.product_id
WHERE o.region = 'EU'
AND o.date >= '2025-10-01'
AND o.date < '2026-01-01'
GROUP BY p.name
ORDER BY total DESC
LIMIT 3;Widget Pro (€2.4M), Sensor X (€1.8M), Module Z (€1.2M). Check the definition and source orders before using this answer.
Show top 3 products by recorded order value in the EU for Q4 2025
Q4 2025 result from EU order records:
| # | Product | Order value |
|---|---|---|
| 1 | Widget Pro | €2.4M |
| 2 | Sensor X | €1.8M |
| 3 | Module Z | €1.2M |
Translate agreed business questions into testable database queries
Map business terms to the relevant schema and report definitions
Validate generated queries before they run on permitted data
Apply user access rules and retain a reviewable query history
Assess the database and integration options in your environment
A user asks a question in everyday language.
A model proposes a query using approved schema context.
A validated query runs with the user's permitted access.
Results include enough context to inspect the source and definition.
Choose a recurring task and record the time, errors or missed decisions it creates. We assess the available data and compare an AI approach with search, reporting or rules already available to your team.
Answers are scattered across systems and documents, with no clear source to verify.
People collect and reconcile records by hand before they can make a decision.
Suggestions are hard to trust when the data, rule or reason is hidden.
Purchasing and staffing decisions depend on estimates that are not checked against actual outcomes.
Staff and customers cannot reliably find the right record, document or product.
Each feature needs a defined input, a useful output and a way to check the result. These are the workflows we can assess with your team.
Suggest relevant products from catalogue and permitted customer signals. Compare recommendations with existing search and merchandising.
Use retrieval-augmented generation (RAG) to draft answers from approved documents. Retrieve only the passages the user may access and link the answer to its sources for review.
Estimate demand or sales from available history. Measure error against a baseline and review where the forecast is unreliable.
Use an image to find likely catalogue matches, then let the user inspect and correct the result.
Propose reorder points from inventory, lead times and demand, with human approval for purchasing decisions.
Draft replies or classify incoming requests using approved knowledge. Route uncertain or sensitive cases to the right person.
Extract fields from documents for review, keep a link to the source and measure errors on representative samples.
Compare route or delivery suggestions with capacity, timing and service constraints before operational use.
Start with an internal support team searching one approved policy library. Define a read-only pilot that cites accessible passages and routes unresolved questions to the responsible colleague.
Name the source owner, eligible users, permitted questions and refresh schedule. Propagate permission changes and deleted content. When the available source is missing, conflicting or too old, show that limitation and send the question to a human owner.
A domain owner supplies representative answerable questions plus restricted, unsupported, stale and conflicting cases. Keep a separate evaluation set. Record expected supporting passages and acceptable refusal behaviour before trying the pilot.
Use the same questions for current manual search and the pilot. Measure supported answers, correct refusals, permission failures, review time and cost per completed task. A fast, fluent answer still needs verifiable sources.
Agree acceptance thresholds with the process and security owners before testing. Record answer quality, access failures and operating gaps on the evaluation set. Use those findings to approve a limited rollout, revise the pilot or stop it; resolve critical access failures before expansion.
Use these example inputs to measure usage and review effort before agreeing a monthly budget.
| Cost line | Example planning input | How to validate |
|---|---|---|
| Ingestion and refresh | One source, 1,000 documents, daily update check | Measure parsing, changed documents, indexing and connector work separately. |
| Search and model calls | 20 users × 10 questions × 22 working days = 4,400 questions/month | Measure actual calls, tokens, retries and retrieval per question; apply the selected provider’s rates. |
| Hosting and monitoring | One pilot environment and agreed log retention | Separate fixed capacity from storage and usage charges; verify approved data location and access. |
| Human review and support | Review sample size × minutes per review, plus maintenance effort | Estimate staff time for reviews, data refresh checks, incident handling and evaluation after changes. |
Before launch, agree a spending limit, who receives alerts and what happens when the limit is reached. Assign responsibility for paid support and repeat the agreed evaluation when sources, prompts or models change.
Define the task, test with representative data and agree when a person must review the output.
Tell us the workflow, available data and the decision you want to improve. We will scope a practical way to test it.
Discuss your AI project