SPARKITCited reports as standard.
Every claim in a SPARKIT report that came from the literature carries an inline citation linking back to the source. Citations exist so readers can audit the agent's work — they're meant to be clicked, not glanced at.
About
About SPARKIT
A research agent that makes frontier-quality scientific research a routine capability for any team.
Why this exists
Rigorous literature synthesis is expensive — in time, money, and cognitive bandwidth — for every research team. Well-funded labs pay research assistants and database licenses to do it; teams without those resources mostly skip it. SPARKIT is a research agent that compresses days of literature work into minutes, available behind a single API call. We built it to make frontier-quality research synthesis a routine capability for any team that needs it — not a luxury that scales with institutional budget.
What SPARKIT does
Send a scientific question over HTTP. SPARKIT searches the literature, reads the relevant papers, runs any analyses the question requires, and returns a Markdown report with inline citations — usually in about two minutes.
On the Humanity's Last Exam gold subset, SPARKIT scores 53.0% versus 34.9% for direct GPT-5.5 and 28.9% for direct Claude Opus 4.7. On GAIA it scores 75.6% versus 58.2% for the leading search APIs. The lift comes from the research agent, not a bigger model — the same frontier models, wrapped in an agent that can search, read, run code, and reason in cycles, dramatically outperform their single-turn selves on questions that need real research.
The product is API-first by design. The intended caller is another agent or a researcher's pipeline — not a person typing into a chat box. The agent shows its work: every report comes with the number of searches run, papers read, calculations performed, and sources cited.
We also work with teams that need more than the off-the-shelf agent. Custom engagements include connecting the research agent to proprietary databases, internal corpora, or paywalled sources; tuning the agent for a specific domain or therapeutic area; and building bespoke workflows for institutional pipelines. If your data lives somewhere the standard agent can't reach, that's the conversation to have.
What we believe
Refuse uplift to harm.
Dual-use research of concern, biosecurity work, weapons design — these are not edge cases we hedge around. SPARKIT refuses them at input and screens for them at output. We will miss legitimate questions sometimes; we accept that cost.
Your queries belong to you.
We don't train on them. We don't sell them. The privacy policy is short, hand-written, and means what it says.
Show the work.
Every report exposes the agent's process — searches run, papers read, calculations performed, time spent. Output without process is faith. We don't ask for faith.
Meet people where they are.
Academic researchers get 20% off any paid tier automatically. Try-it is $10 for 5 queries — no card-on-file gotchas. Benchmarks and methodology are public. The same product serves a computational biologist at a Series B biotech and a postdoc with a question.
Honest about what's hard.
SPARKIT is an LLM-driven system and can be wrong. Citations can be misattributed; conclusions can overstate evidence. We say so on every page. We'd rather be useful most of the time and honest about the rest than oversell.
Deploying agents at scale, responsibly
As more research agents enter the field, two failure modes will dominate: invisible hallucination at industrial scale, and agentic uplift for harmful work. We engineer against both. Input and output are screened. Citations are mandatory. The agent's research process is visible to anyone reading the report. None of these is sufficient on its own; together they shift the asymmetry away from the failure modes that matter most.
Get in touch
If you're building research workflows on top of an agent — or want to scope a custom engagement — write us at info@sparkit.science.