Reports That Decide, Not Describe

Custom dashboards, RAG systems, and BI replacements built around how you actually decide. Self-hosted pipelines, citation-first answers, and reporting that surfaces what matters instead of describing what happened.

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Most BI tools were built for a world where data lived in spreadsheets and reports were monthly. Modern businesses generate data continuously, decisions need to be made daily, and the tools haven't kept up. Custom data systems close the gap. Pipelines that flow continuously, dashboards built around the questions you actually ask, and AI-native retrieval that lets non-technical team members get answers from documents and databases without writing SQL.

What we build

What we build in Data

Custom dashboardsReporting built around your real questions, not template KPIs. Fully owned, self-hosted, integrated with your actual data sources.
RAG (Retrieval-Augmented Generation) systemsAI search across your documents, databases, and knowledge bases. Citation-first answers with permission-aware retrieval. Replaces "ask the team in Slack" with "ask the system, get a sourced answer."
Data pipeline engineeringETL/ELT pipelines built around your real data sources. Replaces fragile Zapier flows and manual exports with infrastructure that scales.
Multi-touch attribution systemsTrack value creation across the entire customer journey, not just last-click. Marketing, sales, and product attribution unified.
Operational reportingAutomated weekly/monthly reports that synthesize data into decisions, not just charts. Generated automatically, distributed to the right stakeholders.
Anomaly detection and alertingAI watches your operational data, flags deviations from normal patterns, surfaces issues before they become problems.

Where this lands

Industries that use this most

Healthcare practices use attribution systems to track marketing ROI across multi-location operations. Financial services use compliant reporting pipelines for regulatory submissions. eCommerce uses custom dashboards for inventory, sales, and operational metrics. SaaS uses RAG systems for customer success and internal knowledge management. Logistics uses real-time operational dashboards for dispatch and tracking.

Sample workflow

Sample workflow: RAG system for a SaaS customer success team

01Documents from Notion, Google Drive, Confluence, and customer chat history flow into a unified vector store
02Permission-aware retrieval respects who-can-see-what controls from source systems
03CSM asks "What is our policy on enterprise renewals when CSAT dropped in Q2?"
04AI retrieves relevant policy docs, customer history, and prior renewal patterns
05Answer returned with citations to specific source documents, latency under 500ms

This pattern is from a real Thinkiyo build. Anonymized; full reference available on call.

Stack

Tech we work with

SnowflakeBigQueryPostgresPineconeWeaviateQdrantLangChainOpenAI / Anthropic / open-source LLMsLooker StudioCustom React dashboardsHosted on your infrastructure

Frequently asked about Data

How is this different from buying Looker, Tableau, or PowerBI?

Those tools are excellent at the chart-rendering layer. Custom data systems make sense when the bottleneck is upstream. Broken pipelines, fragmented data sources, or questions that require AI synthesis rather than dashboard filtering. Many clients keep their existing BI tool and we build the pipeline and AI layer underneath it.

How accurate are RAG systems? What about hallucinations?

Modern RAG with proper retrieval and citation enforcement can achieve 90%+ accuracy on well-scoped queries with clear source documents. We build with citation requirements, confidence scoring, and explicit uncertainty handling. If the system cannot find a confident answer, it says so. Hallucination is a system design problem, not an AI problem, and the design pattern is solvable.

Where is data stored?

Wherever you require. Most clients self-host on their own AWS, Azure, or GCP tenant. Data residency, encryption, and access controls are configured to your compliance requirements.

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