Feature 1
Natural-language to validated SQL: business users type questions in plain English; the system generates schema-aware SQL, runs it under row-level security, and returns charts plus prose explanation.
Turn natural-language questions into accurate, schema-aware reports in seconds. A revenue lead asks "what drove churn in Q2 enterprise accounts?" and gets a formatted answer with charts, the underlying SQL, the data sources, and an auditable explanation of how the number was computed — without writing a query, opening a BI tool, or filing a ticket with the data team. The MVP ships in 2-3 weeks, connects to your existing warehouse and operational systems (Snowflake, BigQuery, Postgres, Salesforce, HubSpot, NetSuite), and includes the governance, row-level security, and prompt-evaluation discipline that separate a useful internal AI tool from a hallucination machine.
An AI reporting automation MVP is one of the highest-leverage internal AI investments a 50-500 person company can make in 2026. The data team is almost always the bottleneck — every business unit has more questions than the analysts can answer, and the queue grows faster than headcount. The right MVP does not replace your data team; it routes the predictable 80% of questions (revenue breakdowns, funnel comparisons, retention cohorts, expense rollups) through an evaluated AI agent so the analysts can spend their time on the 20% that genuinely needs human judgment. SpeedMVPs builds these MVPs in 2-3 weeks against your real warehouse, with eval discipline, governance controls, and integration coverage that pass enterprise security review. The output is a tool your team uses every day, not a tech demo that gets archived after the launch celebration.
Operations and revenue teams in 2026 still spend the majority of their analytical time on the unsexy parts of reporting: extracting data from three or four systems, joining it manually in spreadsheets, validating the numbers against last week's report, formatting charts for the deck, and answering follow-up questions that require re-running everything. Data teams become bottlenecks — the average mid-market data analyst handles 30-60 ad-hoc report requests per month, and the queue keeps growing as the company scales. Worse, the reports that ship are often inconsistent across teams ("customer" means different things to sales and finance), error-prone (copy-paste mistakes are routine), and stale by the time stakeholders read them. Hiring more analysts does not scale: the queue grows faster than headcount. Generic BI tools (Tableau, Looker, Power BI) help, but they require training, dashboard upkeep, and still leave business users dependent on someone who knows SQL whenever they ask a new question.
An AI reporting agent grounded in your actual data warehouse schema, governed by your existing access controls, and instrumented with eval suites that catch hallucinations before they reach a stakeholder. The agent translates business questions into validated SQL using a schema-aware retrieval layer (semantic search over column names, table descriptions, and prior verified queries), executes queries against the warehouse with row-level security applied, formats results into charts and prose, and explains its reasoning so analysts can audit the answer. Every query, response, and source is logged for governance review. Human-in-the-loop checkpoints catch edge cases — questions that can't be answered safely are routed to the data team rather than fabricated. The MVP ships with 3-5 priority workflows fully evaluated, plus the infrastructure to add new domains in days, not weeks.
80%+ reduction
Ad-hoc reports that previously took analysts 30-90 minutes are answered in 10-30 seconds. Recurring reports run on schedule with no manual intervention.
Same-meeting answers
Sales, ops, and finance leaders self-serve in Slack or the web app instead of filing tickets — questions are answered during the meeting, not after.
60-80% freed
Analyst time previously spent on ad-hoc reporting redirects to modeling, semantic-layer maintenance, and adding new domains to the agent's coverage.
Single source of truth
Definitions live in the semantic layer once; every team gets the same numbers because they're grounded in the same dbt models and verified queries.
100% audit trail
Every query, prompt, response, and data source is logged. Compliance and security teams get a defensible record for SOC 2 / ISO 27001 / EU AI Act reviews.
Caught at eval gate
Golden-query regression tests block model or prompt changes that drop accuracy. Unanswerable questions route to humans rather than fabricate.
Natural-language to validated SQL: business users type questions in plain English; the system generates schema-aware SQL, runs it under row-level security, and returns charts plus prose explanation.
Multi-source integration: connectors for Snowflake, BigQuery, Postgres, Redshift, Salesforce, HubSpot, NetSuite, Stripe, Mixpanel, Amplitude, Looker semantic layer, and dbt models out of the box.
Schema-aware retrieval: semantic search over column descriptions, table relationships, and verified historical queries so the model grounds answers in real warehouse structure rather than guessing.
Scheduled and ad-hoc reporting: the same engine that powers "ask anything" Slack queries also runs nightly board reports, weekly revenue digests, and on-call incident reviews.
Governance and row-level security: SSO via Okta, Entra, or Google; RBAC tied to existing warehouse roles; audit logs for every query, prompt, and response; PII masking on configurable columns.
Eval harness with golden queries: a regression suite of verified question-answer pairs runs on every prompt or model change, blocking deploys that drop accuracy below threshold.
Source-of-truth attribution: every metric in every report includes a clickable link to the SQL that produced it and the dataset it came from — analysts can audit any number in two clicks.
Reasoning trace and follow-up handling: the system shows its work ("I joined orders to subscriptions on customer_id, filtered to Q2 enterprise tier...") and gracefully handles follow-ups ("now break that down by industry").
We sit with your data team and 3-5 power users to map the highest-volume reporting questions, the warehouse schema that answers them, and the governance constraints (row-level security, PII columns, retention policy). We then build a golden eval set — 30-50 verified question/SQL/answer triples — that becomes the regression test for every model and prompt change. By end of week one, the evaluation rubric, integration plan, and architecture are signed off.
We implement the schema-aware retrieval layer (semantic embeddings of columns, table descriptions, prior queries), the SQL generation and validation pipeline, the warehouse executor with row-level security, the chart and prose formatter, and the audit logger. The frontend (Next.js + React) ships with Slack and web entry points. Every prompt and model change runs against the golden eval set; nothing deploys that drops accuracy below threshold. Power users start dogfooding by Wednesday of week two.
We harden the production deployment, instrument cost and latency dashboards, run the system through your security review checklist, and do a full launch to a controlled set of users (typically 20-100). We hold daily check-ins to catch new edge cases, expand the eval set with real failures, and tune the system. We end with a documented handover, runbook, and 30-day roadmap for adding new domains, model upgrades, and integrations.
Traditional
$80K-250K over 4-9 months for an in-house build (1-2 engineers + analyst PM) — and most stall before launch
MVP
$18K-45K flat over 2-3 weeks, including evaluation, governance, and integrations
Savings
Live in 3 weeks, not 9 months — and you only commit to a full build if the MVP earns it
An AI reporting MVP is the foundation, not the destination. Once your team is fluent with natural-language reporting, the same retrieval and execution layer extends naturally into proactive analytics: predictive models trained on the warehouse, anomaly detection that pings the right slack channel when a metric breaks pattern, automated narrative summaries of weekly performance, and AI agents that propose next-best-actions instead of waiting for someone to ask the question. The MVP architecture is intentionally designed so each of these expansions is a new module, not a rewrite — typically 2-4 additional weeks per domain rather than another full project.
“Think of the AI reporting MVP as a translator who is fluent in both business questions and warehouse SQL — but unlike a human translator working from memory, this one is reading from a verified manual every time. The semantic layer is the manual. The eval suite is the certification exam the translator has to pass before every deployment. The audit log is the courtroom transcript that proves what was said and where the answer came from. That combination — fast translation + verified manual + courtroom transcript — is what separates a tool your team trusts from a chatbot they quietly stop using.”
Discover more use cases, services, technologies, and insights
Query status, run actions, get recommendations—across Jira, PagerDuty, AWS. Single interface for ops teams.
A powerful JavaScript library for building user interfaces, particularly single-page applications. React enables developers to create reusable UI components and manage complex state efficiently.
The React framework for production. Next.js provides server-side rendering, static site generation, and API routes out of the box, making it ideal for building high-performance web applications.
Outdated donor tracking systems, sluggish communication tools, and fundraising campaigns that relied more on goodwill than modern engagement strategies.
The complete, ordered process to build an AI MVP in 2026 — from validating the idea to picking models, shipping, and iterating, with concrete tools and pitfalls.
Schedule a complimentary strategy session. Transform your concept into a market-ready MVP within 2-3 weeks. Partner with us to accelerate your product launch and scale your startup globally.