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What Research Teams teams need most
WHAT WE BUILD
Data platforms & applied AI
Literature & Document Search
Semantic search across papers, interviews and internal reports you own.
Annotation Workspaces
Highlights, codes and notes shared on the same document.
Finding Synthesis
Themes assembled from annotated passages, with every claim linked back.
Citation Tracking
Sources attached to statements and carried into exported work.
Source Integrity Checks
Retractions and duplicate entries flagged against the library.
Internal Knowledge Base
Past findings searchable by topic, method and outcome.
Where Research Teams teams lose time
A research team reads far more than it can remember. Papers, interviews and internal reports accumulate, each person keeps notes in their own format, and a question answered two years ago gets researched again by someone new. The material is valuable and largely unsearchable.
We build search and annotation over the corpus. A query returns passages with their sources, annotation captures the judgment in a shared place, and findings are written once with citations that stay attached to the claim. New work starts from what the team already knows.
01
Notes trapped per person
One researcher's annotations on a key paper sit in a private document, so the next person reads it without that context.
02
Summaries without sources
A synthesized paragraph loses the link to the studies behind it, and checking the claim takes an afternoon of searching for the original.
03
Search misses synonyms
Keyword search fails on the term the field used a decade ago, so relevant work appears not to exist at all.
HOW THE BUILD RUNS
OUR PROCESS
Step 01
Walk the workflow
We sit with the people who do the work and follow one job from first contact to final invoice. The gaps show up quickly once nobody is describing them from memory.
Step 02
Model the data
Bookings, orders, patients or jobs become one set of records with clear owners. We agree on the fields before any screen gets built.
Step 03
Build in stages
The costliest step ships first, usually the one where revenue or time leaks. Each stage goes live while the next one is being built.
Step 04
Hand over and support
Your team gets training, written runbooks and a support window. We stay close through the first month of real use, then step back.
SYSTEMS WE CONNECT TO
INTEGRATIONS
We build around what you already run. These are the systems we most often read from, write to, or replace one part of, without asking your team to start over.
Data warehouses and lakes
Identity and access systems
Help desk and CRM tools
Document storage
Monitoring and evaluation tools
THE TARGET
6-8 wk
Pilot to Production
Typical timeline for a scoped, evaluated AI pilot.
THE EXPERIENCE
100%
Evaluation Coverage
Every AI output path tested before release.
THE COVER
24/7
Guardrails
Monitoring, fallbacks, and cost controls running continuously.
THE HANDOVER
1
Source of Truth
A single governed warehouse behind reporting and AI.
OTHER SPECIALTIES
AI & Data Intelligence
QUESTIONS & ANSWERS
Questions about Research Teams
The questions that come up before a ai & data intelligence engagement starts.
Can the tool summarize a large corpus?
It can summarize retrieved passages and point to them, which is different from summarizing a whole library. Citations stay attached so a claim can be checked.
How do you handle proprietary research?
It stays in your environment with access rules and retention you set. We do not train shared models on your material.
Does it replace a reference manager?
No. It connects to one. The reference manager remains the record of truth for citations.
Can it read scanned documents?
Text based scans are handled with optical recognition, and the quality depends on the scan itself. We test a sample from your archive before relying on it, and poor scans stay findable by title.
How do annotations stay consistent?
We agree a small set of codes with the team and keep them in one list, so a tag means the same thing to everyone. New codes are added deliberately rather than invented per document.
Does search work across languages?
It can, with a model that handles multiple languages, and results can be filtered by language. We test with your own material, since mixed language collections often behave differently from clean samples.
HOW WE HELP
The wider team behind this work
Most specialty builds draw on more than one service line. One team, one scope, no handoffs between vendors.
Talk to us about research teams.
Tell us what your team runs today and where it breaks. A real person replies, usually the same day.
Start a conversation
Start a conversation
contact us
Let's Talk About Your Project
Scope the problem and its constraints
Review your current product and stack
Agree on timeline, team, and budget
Cover security and compliance needs
Plan integrations with your tools
Leave with a clear first milestone
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