Research systems that know what they don't know
Automated research pipelines that score coverage, detect gaps, and trace every data point back to its source.
Your research has blind spots
Manual collection doesn't scale
Your team copies data from websites, cross-references spreadsheets, and maintains lists by hand. It works at small scale — but when you need to track hundreds of entities across dozens of sources, manual research becomes the bottleneck. What should be an automated pipeline is a team of people doing copy-paste. When those sources live behind interfaces with no API, screen-based automation can extract data directly.
You don't know what you're missing
Without coverage scoring, there's no way to measure research completeness. Is your intelligence on a brand based on three sources or thirty? Are there entire market segments you haven't looked at? You can't make decisions on data you don't know is incomplete.
Source reliability is untracked
Not all sources are equal, but your research treats them as if they were. A press release, an industry report, and a blog post carry different weight — yet they all end up in the same spreadsheet with no provenance, no date, and no way to verify where the information came from.
From manual collection to systematic intelligence
Intelligence audit
I map your current research process — what you track, where you source it, how you verify it, and where the gaps are. We identify the entities, relationships, and data points that matter most to your business decisions.
Data architecture
I design the entity schema, source registry, and coverage scoring system. Every data point gets a source URL, a date, and a reliability score. The schema models your domain — not generic records, but the specific entities and relationships your business needs.
Build
Production development with automated research pipelines, entity resolution, and coverage tracking. You see real data flowing through the system weekly — sources being scored, entities being resolved, gaps being identified.
Deploy & expand
Ship to production, expand source coverage, tune reliability scoring. The system grows with your intelligence needs — new entity types, new sources, and new analysis capabilities plug into the existing framework.
Every fact sourced, every gap scored
Every fact in the system has a source URL, a date, and an author. No unsourced claims, no undated information, no untracked AI outputs. If it's in the database, you can verify where it came from.
Coverage scoring tells you what you know and what you don't — per brand, per entity type, per data category. Gap analysis identifies the specific intelligence missing from your picture, so research effort targets what matters most.
Register price sources, sweep tracked URLs, view pricing status across the portfolio — all automated. The competitive intelligence that used to take a team of analysts runs as a scheduled pipeline. Read how it was built.
From raw data to intelligence
- Entity resolution with alias-aware lookup across brand names, calibre references, and supply-chain entities in multiple languages
- Coverage scoring and gap analysis measuring research completeness per brand — surfacing exactly where intelligence is missing
- Automated price tracking across registered sources with competitive pricing intelligence and portfolio-wide status views
Built for provenance
Common questions
What does 'coverage scoring' actually mean?
Coverage scoring measures how complete your intelligence is on a given entity. If you're tracking a brand, the system knows whether you have pricing data, supply-chain information, recent news, and financial metrics — or whether there are gaps. The score tells you where additional research will yield the most value, so your team focuses effort where it matters most.
How do you handle data from unreliable sources?
Every source in the system has a reliability ranking. An industry report from a recognised institution carries more weight than an unverified blog post. The ranking is transparent — you can see why a data point is scored the way it is and override it if your domain expertise says otherwise. This is the same principle behind governed AI workflows: decisions are traceable, not opaque.
Can this connect to our existing data sources?
Yes. The research pipeline uses typed MCP tools for data access — adding a new source means adding a new tool, not rewriting the system. Whether your data lives in a CRM, a financial platform, a content management system, a mobile application, or behind an API, the integration pattern is the same: structured extraction with source attribution.
How is this different from a BI tool like Tableau or Power BI?
BI tools visualize data you already have. A custom research system gathers, validates, and scores data you don't have yet. It's the difference between a dashboard and a pipeline. BI tools answer 'what does our data say?' A research system answers 'what do we know, what are we missing, and how much can we trust what we have?'
How does the system stay current as data sources change?
Every research pipeline includes source-health monitoring. If a source goes offline, changes its structure, or stops returning data, the system flags it immediately rather than silently serving stale results. Refresh cadences are configurable per source — market prices might update hourly, regulatory filings weekly, and industry reports on publication. The same governed automation patterns that manage workflows handle data freshness: every update is logged, every gap is visible, and your team decides the priority.
Stop researching blind.
Let's talk about turning manual data collection into systematic intelligence.