Cross-FunctionalProduct
Prior research. Found before it's repeated.
Searches prior experiments, research notes, and technical reports to surface related work before a team starts something that's already been tried — every match cited to the source record. Built for R&D and product teams running dozens of research threads at once.
Related work found in seconds, cited. Built on Sphere AI Foundry.
rd_discovery — query #RD-40119LIVE
00:00Ingest → question: prior work on edge-caching latencyread
00:02Search → experiment logs + technical reportspassed
00:03Match → 3 related experiments found, 2022-2024passed
00:04Cross-check → one experiment concluded approach was abandonedflagged
00:05Answer → cited summary returnedsealed
response time 6ssources cited 3/3researcher review optional
Organizations around the world trust us
Built on Sphere AI Foundry·Every match cited to source·21 years, 300+ clients
Prior-work discovery doesn't scale with team size
A single 'has anyone tried this before' question means someone searching old research repos, asking around on Slack, and hoping the person who ran that experiment two years ago still remembers the outcome. At low volume that's manageable. Across dozens of active research threads and constant team turnover, it's the reason the same failed approach gets tried again eighteen months later.
80%
faster prior-work discovery vs. manual search and asking around
3
related records surfaced per query, on average
100%
of matches traceable to the source experiment or report
Question in, cited prior work out
Four steps, every one logged and citable back to the source record.
01
Ingest
Reads the question directly from your systems
A plain-language research question — asked from your knowledge base, wiki, or a chat interface. No separate upload step.
Example query
"Has anyone tried edge-caching to reduce this latency before?"
02
Search your records
Searches experiment logs and technical reports together
Prior experiment logs, technical reports, and research notes — searched together by topic and technique, not just keyword match.
Search
Semantic search across experiment logs, 2022-2026
03
Cite every match
No match without a citation
Each match links back to the exact experiment log or report it came from — the citation travels with the finding, not as a separate lookup.
Cited finding
Edge-caching was tested in EXP-4471 (Nov 2023) and abandoned due to cache-invalidation complexity at this scale.
cited to Experiment Log EXP-4471, 2023-1104
Classify & route
One disposition, one consolidated summary
The agent returns No Prior Work Found, Related Work Found, or Duplicate, with a consolidated summary of prior findings routed to the researcher before work begins.
Disposition
No prior work foundRelated work found ✓Duplicate — in progress
10 fields, every one cited
What the agent returns with every query.
Discovery summary & disposition
Related experiment/report citations
Technique or topic match score
Outcome summary of prior work
Team or researcher who ran it
Date and recency of prior work
Duplicate-work flag
Suggested next steps or open questions
Consolidated citation list
Researcher review checklist
Typical inputs
What the agent reads, and what it connects to.
Documents
- Experiment logs and lab notebooks
- Technical reports and internal research papers
- Prior PRDs or design docs on related topics
Systems
- Research knowledge base or wiki
- Document repository
- Chat or helpdesk interface
Is this the right fit?
Built for a live research repository — not a one-off search.
✓ Works best for
- R&D and product teams running 10+ active research threads
- Organizations with a documented experiment/research repository
- Leadership wanting duplicate work caught before it starts
Too small for
- A single question a team lead already knows the answer to faster than configuring the search
- Replacing a researcher's judgment on whether prior work actually applies
- Teams with no documented research repository to search
Grounded in the record, not a black box
Compliance-first, the same standard every Sphere agent is held to.
The agent surfaces only documented prior work — it returns 'no prior work found' rather than guessing when nothing matches. Every match sits between a new research question and the researcher's own judgment on relevance, never past it.
Matches reference the specific experiment log or report directly — the same audit-trail standard every Sphere agent on the Foundry is held to.
Prerequisites documented repository
A documented experiment or research repository is required before the agent will search with confidence.
Human review optional
A researcher reviews matched prior work for relevance — the agent surfaces candidates, not conclusions.
Source grounding record-cited
Matches reference the specific experiment log or report directly in every result.
Foundation Sphere AI Foundry
Access controls and audit trail configured here carry forward to every other agent you deploy.
Frequently asked
Does the R&D Knowledge Discovery Agent replace a researcher's judgment?
No. It surfaces documented prior work with citations — the researcher still decides whether it actually applies to the new question.
What sources does it search?
Experiment logs, technical reports, and prior PRDs or design docs out of the box. Additional sources can be added during onboarding.
How does it integrate with our knowledge base?
It reads directly from your research wiki, document repository, or a chat interface — no separate upload step required. Integration scope is confirmed during deployment planning.
Is every match traceable to a real record?
Yes. Every match cites the specific experiment log or report it came from — there is no match without a citation back to the source.
How is this different from a generic wiki search?
It searches by technique and topic semantically and cross-references outcomes, not just keyword matching a title. It runs on Sphere AI Foundry, so the access controls and audit trail configured here carry forward to other agents you deploy.
Hear from
our clientsHear from our clients
Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
Ben Crawford
Senior Product Manager at Enova Financial
I would expect to be delighted. It's been a really positive experience, working with Sphere, and I would expect you to have the same.
Mark Friedgan
CEO at CreditNinja
Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.
René Pfitzner
Co-Founder at Experify
Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.
Bruce Burdick
Chief Information Officer at Integra Credit
We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.
Jemal Swoboda
CEO at Dabble
The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.
Arthur Tretyak
Founder and CEO at IntegraCredit
With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.
Lee Ebreo
VP of Engineering at Credit Ninja
These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.
Selah Ben-Haim
VP of Engineering at Prominence Advisors
Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).
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Start here
See it on your own research backlog
Bring a real research question to the demo. We'll show the discovery live — related work, citations, and the outcome summary, from your own repository.
Looking for a different workflow? Browse the full Agent Catalog.
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