Primary outcome
turn raw data into faster operational decisions
What we ship
analytics copilot, metric dictionary, insight workflows, and executive narratives
KPIs we report on
time to insight, dashboard adoption, decision cycle time, and anomaly response
What "automating data analytics with AI" actually means
Automating data analytics with AI is not a single product you buy. It is a workflow you redesign around AI as the operating layer. The agent handles the high-volume, high-structure tasks. Humans handle edge cases, exceptions, and trust-sensitive decisions. The system is instrumented to measure time to insight, dashboard adoption, decision cycle time, and anomaly response and improve weekly.
What changes by industry is the systems the agent integrates with, the data it retrieves over, the controls it operates under, and the KPIs it has to defend. The architecture is similar; the integration and the controls are different.
The architecture we use for AI data analytics
- Frontier LLM — Claude, GPT-4-class, or Gemini. We benchmark candidates on a labelled test set during Discovery.
- Retrieval layer over your approved internal sources, with source citations on every output.
- Tool use for reads and writes against your operational stack (CRM, ERP, ticketing, data warehouse).
- Reviewer queue for low-confidence outputs. Confidence thresholds set per workflow.
- Evaluation harness — labelled test set, weekly accuracy reports, regression alerts.
- Versioned prompts and reviewer-action audit logs for traceability.
5 industries with a scoped engagement page for data analytics. Each is a dedicated build with industry-specific systems, controls, and pricing.
How do you automate data analytics with AI?+
We map your existing data analytics workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure time to insight, dashboard adoption, decision cycle time, and anomaly response from day one and improve weekly.
What is the best AI agent for data analytics?+
There is no single off-the-shelf "best" agent for data analytics — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.
What does AI data analytics cost?+
Three phases, billed separately. Discovery sprint: $6k. Build engagement: $22k–$30k. Run retainer: $3k–$5k / mo. ~$34k–$60k typical year 1 (60% take the run option for ~6 months). Pricing varies slightly by industry — see the industry-specific pages below.
How long does it take to deploy AI data analytics?+
Thin-slice in production in ~6 weeks after Discovery, full Build phase over 7-10 weeks. By day 90, time to insight, dashboard adoption, decision cycle time, and anomaly response is instrumented and you have a baseline against which to expand to adjacent workflows.
Which industries do you build AI data analytics for?+
5 industries listed below have a scoped engagement page for data analytics, each with industry-specific systems, controls, and KPIs. Common starting industries include Retail, Construction, Real Estate, SaaS, Consulting, and others. Don't see yours? We build for any sector — tell us about your workflow and we'll scope it.
What do we own, and what do you own?+
We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
Selected portfolio
Real builds tied to data analytics
A rotating selection of engagements where data analytics was a primary driver, drawn from our active portfolio. Sectors and scope are accurate; client identities are withheld under engagement NDAs.
Q1 2026
AI pricing system for startup founders — 9-step foundation + personalised AI brain
Founder-led pricing-strategy AI SaaS · DACH
First AI-powered pricing platform for startup founders. Structured 9-step pricing-foundation flow (product, customers, competition, costs, boundaries, model, strategy), personalised AI brain that learns from each business over time, two subscription tiers with money-back guarantee. Built end-to-end including billing, AI orchestration, and onboarding.
- Next.js + TypeScript
- Multi-LLM orchestration
- Subscription billing
Q1 2026
AI-powered interior design platform — generative room concepts for the MEA market
AI interior design SaaS · MEA region
Vertical AI SaaS for interior design in the Middle East: image-conditioned generation tuned for local taste profiles, room-by-room concept workflow, project export for designers and clients. Built with a market-specific dataset and an evaluation loop on regional aesthetic baselines.
- Next.js + image generation pipeline
- Regional taste-profile tuning
- Designer + client export flows
Q3 2025
Specialist automotive software-optimization site — multi-brand chiptuning
Vehicle optimization specialist · DACH region
Marketing site for an automotive software-optimization specialist serving multiple regions: brand-by-brand service architecture, technical service descriptions accessible to non-technical buyers, lead capture per service, regional-catchment SEO foundation.
- Next.js + responsive
- Multi-brand IA
- Regional SEO
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.