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Solutions
Stop arguing about whose number is right.
When every department maintains its own spreadsheet, management meetings become reconciliation meetings. We build the layer that produces one agreed set of numbers — defined once, calculated in one place, and visible without asking anyone to run a report.
The business problem
The problem is rarely a lack of data. It is that the same metric is defined differently in three systems, each report is built by hand, and by the time the numbers are agreed the month is over. Decisions end up being made on whoever's spreadsheet was presented most confidently.
- Three systems, three different answers to the same question.
- Monthly reporting is a person-week of manual assembly.
- Managers cannot see their own numbers without asking someone.
Before and after
How it runs today
- An analyst exports data from several systems into a spreadsheet.
- They reconcile mismatches by hand, applying undocumented judgement.
- A deck is assembled and circulated once a month.
- Someone asks a follow-up question, and the whole exercise repeats.
How it runs afterwards
- Metric definitions are agreed once and written down as the reference.
- Data is collected on a schedule with validation and freshness checks.
- Dashboards show each manager the numbers they are accountable for.
- Anomalies trigger an alert rather than waiting for month end.
- The monthly report is generated, and the analyst does analysis instead of assembly.
How RAIS approaches it
Agree definitions before building anything
What counts as an active customer, when revenue is recognised, what a qualified lead is. This is a business conversation, and skipping it guarantees the dashboard will be distrusted.
Calculate once, display many times
Metrics are computed in one place and consumed everywhere else. Recalculating the same figure in each dashboard is how organisations acquire three versions of the truth.
Build for the decision-maker
A dashboard nobody opens is a failed project regardless of technical quality. We design around a specific person, a specific decision and a specific cadence.
Show data quality honestly
Where a number is estimated, incomplete or stale, the interface says so. Hiding it is how trust is lost permanently the first time someone checks.
Services involved
AI Agents
Agents scoped to one workflow, integrated with your systems, always reviewable by a person.
Related use cases
Management Dashboards
One agreed set of numbers, visible to the people accountable for them.
API Integration & Orchestration
Connect systems properly — with validation, retries, idempotency and an audit trail.
Document Processing
Extract structured records from invoices, contracts and forms, with a review threshold before anything is written.
Internal Knowledge Assistant
Let your team find the answer that already exists, with permissions respected and sources shown.
Typical integrations
ERP
Orders, inventory and finance records synchronised through a validated adapter rather than a nightly spreadsheet.
CRM
Read and write deals, contacts and activities so qualified enquiries land where sales already works.
1C
Exchange with 1C configurations through supported interfaces, with reconciliation and a quarantine for bad records.
Google Workspace
Sign-in, Drive, Sheets and Calendar as data sources and destinations for automated workflows.
Microsoft 365
Identity, mail, calendars and document libraries, with permissions respected rather than bypassed.
REST API
Documented, versioned and authenticated endpoints — both consuming yours and exposing ours.
Frequently asked
Do we need a data warehouse?
Not always. For many mid-sized companies a well-modelled operational database and scheduled collection is enough, and it is dramatically cheaper to run. We recommend a warehouse when data volume, source count or history genuinely require it — not as a default.
Can you use our existing BI tool?
Yes. The valuable, difficult part is the definition and pipeline layer beneath it; the visualisation tool is comparatively interchangeable. If your team already knows one, keeping it is usually the right call.
Will AI generate insights for us?
AI is genuinely useful here for anomaly detection, narrative summaries of what changed, and natural-language querying over defined metrics. It is not useful for deciding what your metrics should mean — and a system that guesses at definitions produces confident nonsense.
Next solution
Digital Product Launch
Take an internal capability to market as a product people can buy and use.
