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Common Questions About Decision Intelligence

How is this different from Tableau, Power BI, or Looker?

Traditional BI tools need manual data prep, SQL skills, and pre-built dashboards. Our solution offers natural language queries (no SQL needed), AI insights that answer questions you didn't think to ask, and alerts that flag issues on their own. It also gives you one unified data layer, and we handle the messy integration work.

What if our data is messy or incomplete?

This is common. Many of our projects start with data quality issues. We audit your data during discovery, set up automated cleaning and checks, score and monitor data quality, and improve it over time. We'll tell you upfront if your data isn't ready and suggest a prep phase if needed.

Can this integrate with our legacy systems?

Yes. We've connected to older mainframe systems, custom-built ERPs with no APIs, on-premise databases behind firewalls, SaaS tools with rate-limited APIs, and spreadsheets that people update by hand. If your system can export data (even manually), we can work with it.

How accurate are the AI's answers?

We measure accuracy on your own questions before launch. We use Text-to-SQL validation, RAG for sourced answers, confidence scoring so the system says when it is unsure, and human review for critical decisions. We share the test set and the results. We do not quote a fixed accuracy figure, because it depends on your data.

What happens if our data schema changes?

We build flexibility into the system. New columns, records, and format changes are handled automatically. For major changes like new tables or business logic, we give your team documentation to make the updates, or offer a monthly retainer for ongoing support. We don't bill hourly. We agree retainer terms with you during scoping, based on what support you need.

Can we use our own LLM or keep data on-premise?

Yes to both. LLM options include OpenAI GPT-4, Anthropic Claude, Azure OpenAI, AWS Bedrock, or self-hosted open-source models. For data residency, we support cloud deployment in your account, on-premise in your data center, hybrid, or fully air-gapped solutions.

How do you handle security and compliance?

Security is built in from day one: encryption at rest (AES-256) and in transit (TLS 1.3), role-based access control, audit logging, SSO integration, and MFA. We design with SOC 2 guidance, design for GDPR requirements, and add HIPAA-aligned safeguards with a signed BAA where needed.

How much does decision intelligence cost and how long does it take?

Projects start from $20K at a fixed price, and a full build is delivered in 4 to 5 months. Pricing depends on data complexity (5-10 sources versus 20+), feature scope (50 versus 200+ pre-built queries, dashboard complexity), compliance requirements, and scale (concurrent users, query volume). A full build includes integration with 5+ systems, the unified warehouse, the natural language interface, dashboards, and 30 days of post-launch support.

What's the difference between this and Data Pipeline Engineering?

Data Pipeline Engineering builds the plumbing: unifying and cleaning your data in a warehouse. Decision Intelligence & Analytics is the layer executives and analysts talk to, the natural-language queries, dashboards, alerts, and forecasts. If your data already lives in one clean warehouse, we can start with this layer. If it's still scattered across systems, we usually suggest the pipeline work first.

Do executives need to learn SQL or a new dashboard tool?

No. The point of the natural language interface is that an executive types a question in Slack or Teams the way they'd ask an analyst, and gets a sourced answer back. Dashboards exist for the metrics people check daily, but the conversational layer is there for everything else, with no SQL or training required.

How reliable are the forecasts, and what if they're wrong?

Every forecast comes with a confidence range, not a single false-precision number, and we backtest it against your own historical actuals before launch so you know how far to trust it. Forecasting is uncertain. Our job is to make that uncertainty visible, not hide it behind a clean-looking chart.