Most AI impresses. Ours delivers.
Custom Gen AI, Power BI copilots, chatbots grounded in your own data, agentic workflows, and the cloud infrastructure to run them all. Built for the work you actually do, benchmarked against ground truth before launch, and observed after.
Six pillars of Analytics & AI
Built on top of our market research foundation. A full analytics practice covering Gen AI, real-time dashboards, and the agent work that sits between them.
Data Visualisation
Interactive dashboards built on Power BI, Tableau, Looker, or a custom React stack. The aim is reports that load quickly, age well, and tell a coherent story rather than just dumping numbers on a page.
Data Analytics
Statistical work on top of your raw data: segmentation, driver analysis, trend detection, and the unglamorous data preparation that makes the analysis worth trusting.
Gen AI Modelling
LLM pipelines grounded in your own data. Retrieval, fine-tuning, auto-drafting, and synthesis tasks built on Azure OpenAI, Claude, or open-source models hosted in your cloud.
AI-Integrated Dashboards
Chat panels sitting inside the BI report itself. Stakeholders ask plain-English questions, get grounded answers tied to live data, and can drill into the underlying numbers without leaving the dashboard.
AI-Based Data Reading
Pipelines that turn PDFs, scanned forms, survey exports, and scraped web data into structured datasets analysts can actually use. Validation and human-in-the-loop review where the cases call for it.
Agentic AI Workflows
Multi-step agents that handle longer-running jobs on their own. They query systems, work with the results, and escalate to a human when they're unsure. For the work a single chat exchange can't cover.
We build dashboards on every major platform
We work on whichever BI tool your team already uses. Power BI, Tableau, Looker, or a custom web stack when none of those fit. No pushing you onto the one we happen to prefer.
Multi-page Power BI workspaces with star-schema models, DAX measures, row-level security, and (where it earns its place) embedded Copilot for natural-language Q&A.
Exploratory Tableau work with LOD expressions, story points, and Tableau Server or embedded deployments where a portal needs the same view as the analyst team.
LookML semantic models and Looker Studio dashboards on top of BigQuery, GA4, and the rest of the Google Cloud data stack.
When off-the-shelf BI doesn't fit, we build the dashboard in React with Recharts, D3, or Chart.js, white-labelled and embedded inside your product.
Statistical work and Streamlit apps for the cases that don't sit cleanly inside a BI tool. Reproducible pipelines, exploratory analysis, and lightweight internal tools.
Any of the dashboards above, extended with a chat assistant, auto-generated narratives, or anomaly alerts. Added where it actually helps the user, not as a tickbox.
AI chatbot integrations
Patterns we've built across industries. Some are customer-facing standalone bots; others sit inside a Power BI workspace where the analytics already live.
Research Insights Assistant
A conversational layer over a multi-year brand tracker. Staff in regional teams ask shopper-preference questions in plain language and get answers cited back to source survey data, without needing to open a dashboard.
Patient Feedback Chatbot
A pattern we've built for healthcare clients: aggregate patient survey responses across sites, then let clinical leads query satisfaction trends and benchmark against peer groups through a secure chat interface.
Financial Services Research Copilot
Compliance-aware chat over quarterly investor sentiment surveys. PII redaction, single sign-on, full audit logging, and on-demand plain-language briefings drawn from the underlying research.
Brand Health Dashboard Assistant
A chat panel sitting inside a Power BI brand-health report. Users ask questions like 'why did awareness fall in this region last quarter' and the assistant generates the DAX, runs it against live data, and explains the result.
NPS Copilot
An AI layer inside a customer satisfaction workspace that interprets NPS movements, surfaces the likely driver combinations, and drafts a recommended-action narrative the team can edit before sharing.
Dealer Performance AI
Mystery-shopper and CX survey data piped into Power BI on a streaming dataset, with an embedded assistant generating region-level performance narratives and flagging outlier sites for the team to review each week.
Four steps, no theatre
We've seen enough AI projects fail on the same predictable things. This is the process we run to avoid them.
Understand
Every project starts by mapping the data you already own, the users the system has to serve, and the constraints it has to live inside. No fixed methodology, and no model chosen before we know what it's for.
Design
Pick the right architecture for the job. Sometimes that's a retrieval system on top of an existing model, sometimes a fine-tune, sometimes an agent, and sometimes nothing more elaborate than a well-designed prompt with proper guardrails.
Build
Ship a working system with the boring parts done properly. Evaluation harnesses, logging, kill switches, human review for the edge cases, and benchmarks against ground truth before anything reaches production.
Operate
Monitor for drift, catch regressions when the next model release behaves differently, and keep the system useful after launch. Nothing we ship is a demo you have to babysit.
The tools we tend to reach for
Choice is driven by your data-residency, cloud relationship, and budget — not by whichever vendor we happened to talk to last week.
- •Azure OpenAI (GPT-4o, GPT-4.1)
- •Anthropic Claude (Sonnet, Opus)
- •OpenAI API
- •Open-source: Llama, Mistral, Qwen
- •Vision-language for OCR: GPT-4o, Claude, Gemini
- •LangChain / LangGraph
- •LlamaIndex for retrieval
- •CrewAI, AutoGen for agents
- •Semantic Kernel (Azure)
- •Ragas, promptfoo for evaluation
- •Azure, AWS Bedrock, GCP Vertex
- •Vector stores: pgvector, Pinecone, Weaviate
- •Kubernetes (EKS, AKS, GKE)
- •Terraform, Pulumi, Bicep
- •OpenTelemetry, Langfuse for tracing