Industry’s hardest questions.
Academic rigor.
Your domain expertise meets our research team. Together, we develop original methods and submit the findings for peer review.
Research co-authored by contributors from both organizations, submitted to a jointly selected peer-reviewed venue.
AI research with leading enterprises
For large companies moving into a new field, and startups whose technical depth is not yet visible from the outside. You set the direction. We conduct the research with you.
Problem domain, application context, available data, and technical review at agreed checkpoints.
Literature review, method design, experimentation, manuscript preparation, and reviewer response.
Submitted to a jointly selected conference, co-authored by contributors from both organizations.
A typical engagement runs three to four months from problem definition to submission, dependent on target conference deadlines.
You specify the research domain and target application.
Both teams agree a defined research question and evaluation criteria.
Method development, experimentation, and manuscript preparation.
Manuscript submitted to the jointly selected venue.
Rebuttal preparation and response to reviewer comments.
The manuscript is published with both organizations listed as co-authors on the byline.
Reviewer feedback is incorporated and the manuscript is resubmitted to the next appropriate venue within the same engagement.
The people who did the work are the authors. Contributors from your team who shape the problem, the data, or the experiments are named on the manuscript. A fee alone never places anyone on a byline.
Author order follows contribution and is agreed in writing before submission, with both affiliations on the record.
The collaboration and its funding are disclosed in the paper, following the venue’s conflict-of-interest and funding-statement requirements. Nothing is hidden from reviewers or readers.
Bylines are earned by contribution, never purchased. Client contributors are authors only when they did author-level work.
We report results as the experiments produce them, negative findings included. The venue’s review process is untouched. There is no acceptance guarantee.
Original methods, benchmarks, and investigations. Published work alongside research in review and preparation.
Time-series foundation models
Numerical reasoning
Multi-agent systems
Causal reasoning · Autonomous driving
Depth-guided diffusion
Video understanding
Monocular 3D reconstruction
Pose estimation · Automated driving
Semi-supervised learning · 3D pose
One problem statement, one manuscript, three to four months to submission.
Final pricing depends on the research scope and is agreed before work begins.
Talk to usPeer review outcomes cannot be guaranteed by either party. The engagement commits to the research and publication process through to a decision. Following rejection, reviewer feedback is incorporated and the manuscript is resubmitted to the next appropriate venue within the same engagement.
Build an in-house team, partner with a university lab, or collaborate with a research group like ours.
| What matters | Hire researchers in-house | Partner with a university lab | FAST CODE AIResearch collaboration |
|---|---|---|---|
| Research direction | Client-defined | Shared with the institution | Client-defined |
| Cost structure | Recruitment, salary, and retention of ongoing headcount | Grant funding and institutional overhead | Fixed fee, agreed before work begins |
| Time to first submission | Recruitment and ramp-up precede any output | Set by academic cycles and lab capacity | Three to four months from engagement start |
| Response to rejection | Absorbed internally | Subject to institutional priorities | Revision and resubmission included in scope |
| Attribution | Internal only | Shared with the institution | Both organizations named on the byline |
From people who have worked alongside Arjun and the team.
“Arjun was one of the pioneers”
“Arjun is a great researcher / scientist / and entrepreneur. I first learned about his research at MPI on his movie-reshape work that made a big splash in the community, and subsequently I managed to recruit him to come to NYU as a post doc in our lab, where he kept pushing state of the art research in 3D people estimation with conv nets. This was during a time where conv nets made the next leap, and Arjun was one of the pioneers working in my lab to tackle people tracking with conv nets. He also collaborated with Yann Lecun's lab, as well as kept collaborating internationally with other leading research teams. It was a joy to work with him, he was a very productive and strong researcher!”
Bring us the domain, the context, and the question you cannot answer yet.
Talk to us