Financial Services · Wealth management
Risk Intelligence & Client Portfolio Dashboard
Single-pane risk view across client portfolios for a multi-family office
View case study →Service
AI Analytics & Agents
Claude and ChatGPT workflows grounded in approved data, with calculations checked in the warehouse or semantic model and a person in the loop for consequential output.
Typical timeline · Defined in discovery
Illustrative sample
Not a client result
Calculated result
- Gross margin
- 31.4%
- Prior month
- 34.1%
- Change
- −2.7 pt
- Product mix shifted toward a lower-margin family.
- Freight per unit rose in two regions.
- Average selling price was flat.
Figures are computed from a sample semantic model. The language model explains the result. It is not the source of the numbers.
Challenges
- Business users cannot ask a question without commissioning another report
- AI pilots answer from a pasted export, with no record of the query
- A demonstration has no permissions model, test set, or owner for a wrong number
Outcomes
- Answers computed from approved datasets and existing metric definitions
- Access limited to the data each role is already allowed to see
- A documented path from a workspace pilot to an integrated workflow, when the case holds
Engagement process
01
Discover
Map the decision, the systems that hold the data, and where the numbers disagree.
02
Design
Specify the model, the workflow, and the controls: approved data, permissions, and what a person must review.
03
Build
Implement pipelines, semantic models, dashboards, or analytical workflows in releases the team can review.
04
Validate
Check calculations, access, and outputs against agreed tests before a figure is treated as decision-ready.
05
Operate
Hand over documentation and, where you want it, stay on for monitoring and agreed improvements.
Related case studies
Selected analytics work. These case studies show data platforms, reporting, and automated monitoring. They are not presented as completed conversational-AI deployments.
SaaS
Customer-Facing Analytics & API Integration Platform
Embedded analytics platform with live API data feeds and white-label dashboards
View case study →Oil & Gas · E&P operator
VOC Monitoring & Automated Alerts
Automated anomaly detection for emissions data across Guyana deepwater operations
View case study →What we implement
Four ways AI analytics shows up in the business.
01
Claude and ChatGPT-powered analytics
Business users ask questions in plain language. Answers stay tied to datasets you approve and to calculations defined in the model.
- Questions about sales, revenue, cost, and profitability, within metrics you have already defined.
- Analysis of Excel files and recurring management reports when those files are the system of record.
- Draft management commentary and variance explanations a reviewer can edit before they circulate.
- Room to explore a question without commissioning a new report for every cut.
02
Conversational analytics and business data agents
A natural-language interface over a SQL database, a warehouse, or a governed semantic model. The model selects an approved tool. The database or semantic model computes the result.
- Natural-language-to-SQL limited to approved schemas and reviewed query patterns.
- Semantic-model integration so metrics keep the definitions the business has already accepted.
- Tool calling that runs the analytical code or queries you allow.
- Source citations or query provenance where the platform supports them.
- Calculation tests against known results before a figure is treated as management-ready.
- Role-based access aligned to the data each person is already permitted to see.
- Human review before an action that changes a record, sends a report outside the team, or commits a number to a management pack.
03
Automated analytical reporting
Recurring analysis that currently depends on someone exporting, pivoting, and writing the same commentary every cycle.
- Monthly financial reporting.
- Budget-versus-actual analysis.
- Variance explanations.
- Inventory coverage and exception lists.
- KPI summaries for management reviews.
- Anomaly checks and scheduled alerts.
- Report generation and distribution through a channel you already approve.
04
Production AI analytics and enterprise integration
A demonstration still has to survive permissions, ambiguous questions, model changes, and cost. This is the engineering that makes an analytical workflow operable.
- API integration with Anthropic Claude and OpenAI models, when that is the right interface for the workflow.
- Connections to the warehouse and to the applications that should receive the output.
- Permissions and data security aligned to how access already works in your environment.
- Evaluation sets and regression tests so a prompt or model change does not silently move a KPI.
- Observability for failures, latency, and model cost.
- An audit trail and an approval step for consequential output.
- Deployment and support after the first release.
Deliverables
Grounded question-and-answer workflowsSemantic model or warehouse integrationAutomated reporting packsEvaluation and access controls
Led by Ayush Dubey, Principal Data Platform & AI Consultant.