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Always-on AI research agents that answer and keep you current.
SinglePoint™ puts market and competitive intelligence to work around the clock. Its AI research agents use the same licensed, curated and internal content to conduct multi-step Deep Research, answer questions in plain language and keep newsletters and dashboards current, while linking every claim to its source document.
Four ways SinglePoint’s AI research agents work on one governed content set.
Two of them answer when you ask. Two of them run whether you ask or not. All four read the same licensed, curated, and internal content, and every claim they return carries its source.
Answer when you ask
Conversational searchAsk, refine, keep the thread
Plain-language questions get fast, sourced answers, with memory across follow-ups and reasoning about which content sets apply.
Use it when you need an answer in seconds and the question is well understood.
Specialized agents plan the work, search every relevant source in parallel, validate coverage and findings, and assemble a structured report with a citation on every claim.
Use it when the decision has to be right and you would otherwise commission a study.
Ask when you need to. The rest of the time, the agents are already working.
Conversational intelligence
Conversational intelligence that remembers what you just asked.
Ask in plain language and get a fast, sourced answer. Ask a follow-up and it holds the thread, so you refine rather than start over.
Behind each question it reasons about which content sets actually apply, searches across them, and returns the most relevant citations rather than the first passages that happen to match your wording. Everyday questions, answered in seconds, on the same governed content as everything else.
A SinglePoint conversation. A user asks which competitors launched bispecific antibodies in 2026, and SinglePoint answers that Corvanta Bio, Helix Thera and Norren Pharma launched programs. Each cited source shows why it was chosen: a licensed pipeline report for the most recent pipeline coverage of all three companies, and a conference abstract for primary efficacy data presented in 2026. The user follows up with “Just the ones in Phase II?” and SinglePoint shows what it remembered from the conversation (bispecific antibodies, launched in 2026, the three companies) before narrowing the answer to Corvanta Bio and Norren Pharma. It cites a licensed trial registry record, chosen as the authoritative record of each trial’s current phase.
Memory across the thread
Follow-ups build on what came before, so you narrow a question instead of retyping it.
Reasons about where to look
Works out which content sets apply to the question and searches across them, rather than scanning everything equally.
Most relevant citations, not the first ones
Ranks what it found so the sources attached to the answer are the ones worth reading.
Deep Research
What is SinglePoint Deep Research?
Agentic deep research plans and runs a multi-step search across your governed content, reads full documents (no chunking, so context stays intact), and cites every claim to its source document. It’s the multi-collection work a specialist researcher used to do by hand.
How Deep Research works, in four stages
1
It scopes the work with you
It asks the sharp clarifying questions that narrow a broad topic to exactly what you need, before any searching starts.
2
Plans and runs a multi-step search
Specialized agents sweep your governed content across every relevant collection in parallel.
3
Reads whole documents
No chunking, so context stays intact and nothing important is lost to a fragment.
4
Cites every claim to its source
A structured report where each point links to the document it came from, ready to act on or defend.
A SinglePoint Deep Research run in four stages: scope, search, read, and report. SinglePoint asks whether to cover GPU vendors only or include hyperscalers, and the user includes hyperscalers. Agents search licensed research, news and market data, patents, and internal research in parallel, and coverage is validated. A full 48-page analyst report is read end to end as findings are extracted, then a structured report on the data-center GPU landscape is assembled with a numbered citation on every section. Selecting citation 1 shows the quoted passage from the licensed analyst report. Finally, the Share with team button is clicked and the report is shared with the competitive intelligence team.
“
In their words
I ran a Deep Research report on data-center GPUs, and what impressed me most was the questions it asked. It wanted to know whether to focus on the GPU vendors or include the hyperscalers. I couldn’t believe it knew to ask that, and it was exactly right.
Competitive intelligence leader, global software company
Proactive intelligence
Newsletters and dashboards: intelligence that comes to you.
Most intelligence still waits for someone to go looking for it. Always-on agents run in the background instead, keeping curated dashboards current and pushing what changed to the people who need to know.
A SinglePoint dashboard curated around a market topic updates itself as new content is indexed, and the same intelligence is delivered to readers as a newsletter, with each item linked to its source document.
AI widgets: curated dashboards that update themselves
Build a widget once around a competitor, a market, or a therapeutic area. Agents refresh it continuously against the newest content in your collections, so the dashboard is current every time it is opened rather than as of the last time someone rebuilt it.
Curated once by your team, then maintained by the agents.
Always reflects the most recent indexed content, not a stale snapshot.
Scoped to a competitor, market, topic, or therapeutic area.
Every claim on the widget stays cited and click-through traceable.
200+
dashboards built by a major pharma this way, reaching more than 10,000 people from a two-person team.
Alerts: the change finds the person, not the other way around
Agents watch your trusted sources and notify the right people when something moves. Nobody has to remember to check, and nobody hears about a competitor filing from a colleague three days late.
Triggered by real movement in your sources, not a fixed schedule.
Routed by role and interest, so relevance stays high and noise stays low.
Delivered where people already work, including email and Microsoft Copilot.
Each alert carries the source behind it, ready to forward or defend.
150
newsletters automated by one customer, growing readership to 9,000 employees.
Widgets and alerts run on the same governed content and the same citations as every other answer. Nothing reaches a dashboard or an inbox that the reader is not permissioned to see, and nothing arrives without the source attached. Proactive delivery does not mean loosening governance.
Honest about where it wins
Research expertise built over 20 years.
SinglePoint brings twenty years of research-specific search engineering, enrichment and taxonomies to primary market research, licensed data and conference abstracts.
Use general tools to build the PowerPoint. Use SinglePoint to research the high-stakes question behind it.
Built for high-stakes decisions
Accuracy, traceability and security by design.
Governed content only
Search is restricted to approved internal and licensed sources. Open-web sources do not enter the answer set.
Full citation tracking
Every claim links to its source document. Validate any insight in seconds.
Enterprise-grade security
Your data is never used to train models, and stays within your governed environment.
“
In their words
“What used to take a team of analysts two weeks now happens in a single session, with better source coverage and full traceability.”
VP of Market Intelligence, Fortune 500 enterprise
Common questions
Questions about always-on agents.
What does “agentic” mean here?
The AI plans and runs a multi-step research process across your governed content, reads full documents, and cites its sources, rather than returning a single-shot answer.
How do we trust what it produces?
Every claim is cited to its source document, read in full context, so your team can verify it directly.
How much faster is it?
On the same question across five collections, roughly 9× faster to a usable answer: about 1.5 minutes versus roughly 14.