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 engagementDecision systems
that make the call, and make the case.
Syntheos researches, designs, and builds decision systems for problems that don't have one yet. The methods are our own, grounded in decades of peer-reviewed work across the science of science, AI policy, research security, and computer security and privacy. The team comes from federal R&D, defense, intelligence, and biomedical research. The systems run today at DARPA the Andrew W. Marshall Foundation, and Georgetown.
Each engagement ends with a working system your team runs. Not a deck that ages out in ninety days.
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0M+Publications Indexed
The bibliographic record under our science-of-science work — covering the global scientific literature.
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.
Program Analysis Product
DARPA program offices
DARPA 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 engagement
Research Intelligence Tool
Andrew W. Marshall Foundation
The Marshall archive: making a hidden corpus navigable
A knowledge-graph research console that opens Andrew Marshall's Office of Net Assessment tradition to a new generation of strategists.
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 deployed teaming platform where an AI orchestrator dispatches specialized agents, but phase gates and a delegation contract keep every real judgment in student hands.
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.
Every Syntheos engagement is built for one decision, one institution, one threat model. The four below are not productized offerings. They are case studies of original systems we built for clients who couldn't find what they needed on the market — the shape your engagement takes will follow from the decision you have to make and defend, not from the four below.
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: DARPA program offices
- Deployed analytical site, one per program
- YAML 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 net-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
Knowledge-graph research consoles over specialist corpora. Hybrid retrieval, chat with citation badges, and a visual distinction between verified archive material and AI-generated inference. Built for institutions that want researchers to think with a corpus, not just read it.
Proof: Andrew W. Marshall Foundation
- Knowledge graph built from your documents
- Hybrid vector, full-text, and graph retrieval
- Verified versus inferred node labeling
- Saved investigative threads and filters
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.
Domains
Where decisions
don't tolerate hand-waving.
These are the high-risk decision domains the team came up through. The shape of the work changes from one to the next. The job doesn't: build the system that helps a particular decision get made well, and lets it survive scrutiny.
Federal R&D Strategy & Portfolio Analysis
Federal funders, agency program offices, and the R&D portfolios that have to be defended to Congress, the press, and the next administration. Quantitative portfolio analysis, science mapping, growth-trajectory forecasting, and AI-assisted research-opportunity discovery brought to the call before it's made and to the audit that follows.
Explore capabilitiesFoundations & Strategic Philanthropy
Long-horizon research investment, archive intelligence, and program design for foundations whose decisions have to outlast their staff and define a field. We've built the systems behind specialist corpora, mapped funding portfolios across decades, and modeled where a single grant moves a research community.
Explore capabilitiesHealthcare & Biomedical Research
Translational research forecasting, research-funding portfolios, and clinical-evidence synthesis for medical schools, biomedical agencies, and clinical specialty boards. Built on years of running a research-intelligence operation inside an academic medical center.
Explore capabilitiesAI Policy & Emerging Technology Assessment
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 ours, not borrowed.
Explore capabilitiesDefense & National Security
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 capabilitiesCustom Decision Systems
Some decisions don't fit a playbook. When the stakes are high and off-the-shelf doesn't work, we build around the decision itself: who owns it, what evidence it requires, who will scrutinize the outcome. Human judgment stays in the loop. The reasoning stays visible. Your data stays yours.
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 net-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
The engagement ends when your team is running 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. After that, the system is yours, the data is yours, and the decisions made on top of it are yours.
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