Program Analysis Product
Research program offices
Program analysis sites: evidence you can audit
Per-program analytical sites where every quantitative claim links to a reproducible source and a stated confidence level.
See the engagementThe map of science,
from the people who worked out how to draw it.
Kevin Boyack and Dick Klavans have spent three decades learning to map research from the way papers cite each other. Their topic model is built into Elsevier's SciVal. At Syntheos we rebuild the map from 1.7 billion citation links and use it on the question in front of you, whether you're choosing what to fund or watching where a field is heading.
We work for research funders, foundations, medical schools, and defense and policy offices. Clients include DARPA, the Andrew W. Marshall Foundation, and Georgetown.
The map, checked against what happened
Our 2018 map, checked against 2024.
We took the map as it stood at the end of 2018 and flagged the fields it showed gaining ground, the ones growing fastest and drawing citations from more of science each year. Then we looked at where those fields were six years later.
Of the 1,830 fields it flagged, 514 broke out, outgrowing nine in ten of their peers. That's more than one in four, nearly three times the rate across all fields. The lower a field scored in 2018, the less likely it was to break out.
mRNA vaccines were on the 2018 list. That community held Katalin Karikó's 2005 paper on modified RNA, which won the Nobel Prize in 2023, and its output grew sevenfold by 2024.
We wrote the rules of the test down before we looked.
Share of fields that broke out by 2024
0%
10%
20%
30%
Base rate, 10%
3%
28%
Lowest 2018 signalHighest tenth, flagged
| Tenth of 2018 signal | Broke out by 2024 |
|---|---|
| 1 | 3% |
| 2 | 4.9% |
| 3 | 5% |
| 4 | 5.8% |
| 5 | 6.8% |
| 6 | 7.8% |
| 7 | 9.6% |
| 8 | 11.7% |
| 9 | 17.3% |
| 10 | 28.1% |
A portfolio on the map
One investment that would lift six awards at once.
We took ARPA-E's public record of 113 awards and the 899 papers they acknowledge, and placed every paper on the map. The awards spread across 307 research communities. Fewer than one pair in a hundred shares any ground at all.
The ground they share most is a method, machine learning for materials discovery. Six awards working on batteries, superconductors, alloys and hydrogen all publish there. A breakthrough in that one community would lift all six. That makes it a strong candidate for the next dollar, and no program chart would point you there.
Machine learning for materials discovery
- Lithium-metal batteries
- Nickelate superconductors
- High-entropy alloys
- Alloy thermodynamics
- Hydrogen electrolysis
- Packing optimization
Where your portfolio's knowledge comes from.
We traced every paper ARPA-E's awards cite back to where it was written. Chinese institutions produce 31 percent of that research, against 19 percent of the world's output.
In lithium-metal batteries, which 28 awards draw on, Chinese institutions write 62 percent of the papers and American ones write 12. Reading that work is good science. The question is where capacity at home is thin enough to matter, and the map answers it before a supply shock does.
Chinese institutionsUS institutions
Hydrogen evolution catalysts · 21 awards draw on it
67%3%Lithium-metal batteries · 28 awards draw on it
62%12%CO₂ reduction to fuels · 33 awards draw on it
52%9%Machine learning for materials · 32 awards draw on it
24%29%
Every portfolio has places like these. Some are deliberate and some nobody planned. Send us yours and we'll show you which is which.
Map your portfolioThe map
Every finding on this site happened somewhere on this map.
Each dot is one of 102,903 research communities, placed by how their papers cite each other. Fields that read each other sit close together.
Loading the map
Each dot is a research community, colored by discipline.
Click any community to open its full profile in the explorer.
0.0BCitation Links Mapped
The citation record under the Map of Science. We follow every link between papers in the global scientific literature to find its research communities.
0+Audited Claims
Every deployed system ships with an evidence ledger. Each claim links to the source query that produced it, and the figure reruns live against the warehouse on demand. Auditors can pull any number at random. Regulators can verify the math without our help.
0+Peer-Reviewed Publications
Authored by the Syntheos team across science mapping, AI policy, computer security and privacy, and research security, including work in top-tier venues like Nature, PNAS, USENIX Security, ICLR, FAccT, and Quantitative Science Studies.
0Black Boxes
Every output traces to source evidence through W3C PROV lineage. No opaque models. No silent fallbacks.
Trusted by leaders in defense, healthcare, research, and public policy
Work
Systems we've shipped.
Every engagement ends with a working system, not a slide deck. Each of the four below solved a different shape of problem.
Decision Platform
Andrew W. Marshall Foundation
Deepfield: a modular assessment platform
An eleven-stage strategic assessment pipeline that turns a query into a defensible course of action with full evidence lineage.
See the engagement
Human-AI Teaming System
Georgetown School of Foreign Service (SEST)
Georgetown Wicked Problems Lab: a classroom where AI assists but never decides
A teaming platform in classroom use at Georgetown. The AI listens quietly and helps on request, while phase gates, peer review, and a twelve-rule delegation contract keep every real judgment in student hands.
See the engagement
Research Intelligence Tool
University of Michigan
Pathfinder: research pathway navigation for a map of science
A research pathway explorer that lets users chart and narrate routes between research communities on a global map of science.
See the engagementGovernment & DefenseBuilt for federal
Built for federal
acquisition.
Decision systems built to survive Congressional oversight and meet the standards the DoD AI office is setting.
View Defense CapabilitiesSAM.gov RegisteredNIST 800-171 In ProgressDFARS 7012 In ProgressResponsible AI by Design
Past Performance
DARPA, CSET, Marshall Foundation, Noble Reach
Mission Areas
Competitive Assessment, Technology Scouting, Decision Architectures
Acquisition Vehicles Performed
Direct Award, OTAs, BAAs
What we build
Four systems we built
from scratch.
Each of the four began with a decision someone had to make and stand behind, in a field too large to read unaided. They're original systems, built for clients who couldn't find what they needed on the market. Your engagement will take its shape from your decision. The kind of problem stays the same.
01
Program Analysis Products
Fixed-scope analytical sites where every quantitative claim ships with a reproducible query and a confidence level. Built for R&D funders who have to defend portfolio decisions on the record.
Proof: research program offices
- Deployed analytical site, one per program
- Structured evidence ledger behind every claim
- Reproducible SQL queries against your warehouse
- Python refresh pipeline for standing data
02
Decision Platforms
An eleven-stage assessment pipeline you run yourself. Every stage carries W3C PROV lineage, so any final recommendation traces back through the whole chain to the source evidence. Built for standing strategic-assessment and competitive-intelligence work.
Proof: Deepfield
- Modular pipeline, eleven stages, tuned to your domain
- Knowledge graph, ACH-based reasoning, wargaming
- Adaptive compute budgets per stage
- Full evidence lineage on every recommendation
03
Research Intelligence Tools
Research consoles over specialist corpora. A researcher asks a question and gets an answer cited to the page, with figures that separate what the sources say from what the model inferred. We build a knowledge graph over the corpus when the questions need one. Built for institutions that want researchers to think with a corpus as well as read it.
Proof: Andrew W. Marshall Foundation
- Answers cited to document and page
- Hybrid vector and full-text retrieval, reranked
- Cited versus inferred marking in every figure
- A knowledge graph where the corpus calls for one
04
Human-AI Teaming Systems
Orchestrated AI assistance with human judgment in the loop. A delegation contract constrains what the AI is allowed to do, and database-level phase gates enforce it. Built for learning environments and decision-critical work where the AI must help but never decide.
Proof: Georgetown SEST
- Agent orchestration across fast, deep, and QA tiers
- Delegation contract constraining AI authority
- Database-enforced phase gates
- Voice and text surfaces, instructor tooling
Different problems, different infrastructure. Each engagement ends with a working system your team runs, not a document your team files.
Where to start
Start with
two weeks.
Send us one decision and the material behind it. We run our methods on it and walk you through the result. There's no fee.
By the end of the walkthrough you'll know whether we showed you something you couldn't see before, and we'll give you a fixed price for the full engagement.
Send us a decisionYou sendIn two weeks you get
A research area or a portfolio
A map of the research communities it touches, and where the open ground is.
Read the business caseA program name or a BAA
A live demonstration site running real analysis on the program.
Read the business caseOne strategic question
The charter, intake, and research stages of a Deepfield assessment.
Read the business caseFifty documents from your corpus
The research console answering questions on your material, cited to the page.
Read the business caseOne phase sequence you'd want to pilot
A configured teaming platform with two or three agents tuned to your domain.
Read the business caseWe take one on when the decision is consequential, the structure behind it is hard to see, and the person who owns it will be in the room for the walkthrough.
Who we work for
Where decisions
don't tolerate hand-waving.
These are the people who own the decisions we work on, in the domains the team came up through. The question changes from one to the next. The job stays the same. We build the system that shows where to act and lets the choice survive scrutiny.
Federal R&D Funders & Program Offices
Which programs to start and which to end. The portfolio then has to be defended to Congress, the press, and the next administration. We bring quantitative portfolio analysis, science mapping, growth-trajectory forecasting, and AI-assisted research-opportunity discovery to the call before it's made and to the audit that follows.
Explore capabilitiesFoundations & Strategic Philanthropy
Where a single grant will move a research community, and where it will vanish. Foundations make that call over long horizons, and the decision has to outlast the staff who made it. We've built the systems behind specialist corpora, mapped funding portfolios across decades, and modeled where a grant moves a field.
Explore capabilitiesMedical Schools & Biomedical Funders
Which lines of translational research to back, and what the clinical evidence supports. We do translational research forecasting, research-funding portfolio analysis, and clinical-evidence synthesis for medical schools, biomedical agencies, and clinical specialty boards. It's built on years of running a research-intelligence operation inside an academic medical center.
Explore capabilitiesAI & Emerging Technology Policy Teams
Which emerging technologies deserve attention now, and where research security is exposed. We do AI research-trend analysis, research-security assessment, and long-horizon emerging-technology forecasting, informed by years of work at the institutions that set this policy. The vocabulary, the methodology, and the relationships are our own.
Explore capabilitiesDefense & National Security
Where an advantage can be built or lost over a long competition. Competitive assessment demands synthesis across military, economic, and technological domains over long time horizons. Traceable reasoning. Explicit assumptions you can challenge, test, and refine.
Explore capabilitiesOutside These Domains
The domain matters less than the shape of the decision. We ask three questions. Does the decision carry real money or mission consequence? Is the structure that should drive it hidden from the people making it? Could seeing that structure change the answer? Three yeses and we should talk.
Explore capabilitiesThe team
Small group.
Deep bench.
Syntheos is a small firm by design. Every engagement is staffed by principals — people who came here from the places where high-stakes decisions are actually made.
The Office of Naval Research. The Office of the Secretary of Defense. The MacArthur Foundation. Sandia. The Naval Research Laboratory. CSET. The University of Michigan Medical School. Between us we have decades of peer-reviewed work in science mapping, research evaluation, science policy, AI policy, computer security and privacy, and research security.
You don't get handed off to a junior team. You get the people whose names are on the papers.
“
“Every decision faces two questions: was it right, and how do you know? Everything we build is for both.”
Caleb Smith
Founder & CEO
Our Approach
How an engagement runs.
01
Research
Before architecture, the investigation. We study the decision your team has to make and defend, its evidence base, the failure modes it has historically been caught by, what the literature already settles and what it doesn't. The work product belongs to you.
02
Design
The architecture follows from the research, not from a catalog. A research funder might need a bibliometric pipeline against the portfolio. An archive might need a knowledge graph over the corpus. A strategic-assessment team might need an eleven-stage workflow with provenance lineage end to end. We design the system that fits the decision, not the other way around.
03
Implement
We build it in your environment, on your data, against the security posture your auditors already recognize. Every claim the system produces traces back through W3C PROV lineage to the source evidence. The methods stay yours when we're done.
04
Hand off
Your team runs the system without us. We document the pipelines, train your operators, and stay close enough by phone to fix anything broken in the first month. The system is yours, the data is yours, and the decisions made on top of it are yours. Sometimes that's where it ends. More often the first system shows a client a question they couldn't have asked before, and they bring us back to go after it.
FAQ
Questions From Leaders Who've Been Here Before
These are the questions that come up when we talk to leaders who've seen enough vendor pitches to be skeptical. They're worth asking of anyone in this space, including us.
AI projects often fail because they're framed as technical projects when they're actually organizational change projects. Someone buys a platform, a team builds models, the models perform well on test data, and then nothing changes. The decisions get made the same way they always did, by the same people, using the same inputs. The AI sits on the side, technically functional and organizationally irrelevant.
The usual culprit is starting from the wrong end. "We have all this data, what can we do with it?" is a recipe for building impressive demos that don't matter. So is "leadership wants an AI initiative." These projects optimize for what's measurable by technologists, like model accuracy, processing speed, and data coverage. Decision-makers care about whether they can trust an output, explain it to their boss, and defend it when something goes wrong. F1 scores are not on that list.
We start from the other end. A specific decision that matters, made by specific people, who answer to specific stakeholders. What evidence do they need? What does defensible look like in their world? Where does their current process break down? The technical architecture follows from those answers, not the other way around.
This is less exciting than a general-purpose AI platform. It's slower to show results in a demo. But it's the difference between a system that gets used and a system that gets abandoned.
Initiate Contact
Bring us a decision you have to make and defend.
Tell us about the decision you're trying to improve. We'll schedule a briefing with our principals to understand your environment and see whether the fit is right.
Schedule a briefing