ML Research & Experimentation
When off-the-shelf models stop working, we research. From novel architectures to bespoke training regimes, our applied-science team turns ambitious research goals into production reality.
Where the off-the-shelf model gives up, research begins.
Sometimes a competitive moat genuinely depends on the model itself. The data is unusual, the constraints are unusual, the latency is unusual and no API call is going to solve it. Our applied-science team takes those problems seriously. We start with feasibility, scope rigorous milestones and build novel architecture only where the upside justifies it. The work is publishable when it should beand entirely yours, always.
Feasibility first
Every R&D engagement starts with a 2-4 week feasibility milestone with kill-criteria agreed up front. No quarter-long bets on faith.
You own the IP
Rigorous IP-assignment terms. Co-authored papers when work is publishable, on your timeline, not ours.
Embedded, not isolated
We work alongside your internal scientists, bringing extra hands and a fresh perspective, never trying to replace them.
Why R&D capacity is now a strategic asset.
In a world where every competitor has access to the same APIs, the differentiator increasingly lives in the model itself and the team that can extend it.
The frontier moves faster every quarter. The teams that keep up have applied-science capacity; the ones that don't fall behind invisibly.
A novel architecture or training regime can be a defensible advantage for a year-plus before the market catches up. That's a generation in this market.
Up from under 10% in 2023. The build-vs-buy decision now includes a "research" column and budgets are following.
ML Research & Experimentation services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Novel architecture R&D
When the SOTA paper isn't enough, we extend it. Multi-modal fusion, mixture-of-experts adaptations, attention variants, original work, peer-publishable, production-deployable.
Paper-to-production engineering
Reproducing arXiv papers reliably, then hardening them: stable training recipes, distributed scaling, evals and serving, the gap most teams underestimate.
Custom training regimes
Curriculum learning, contrastive pre-training, RLHF/DPO, distillation, quantization-aware training, tuned to your data and budget.
Synthetic data generation
Diffusion-based, simulation-based, LLM-bootstrapped, when real data is scarce, we generate the long tail to train against.
Benchmark + eval design
Domain-specific benchmarks and evaluation protocols, so your team has the rigor to claim improvements with confidence.
Interpretability & probing
Mechanistic interpretability work, feature attribution, probing classifiers, when stakeholders need to understand, not just trust.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Custom MoE architecture for code generation
A research engagement produced a sparse-MoE variant that beat the previous dense-baseline by 8 pts on HumanEval, at half the inference cost.
Six reasons enterprises run ML Research & Experimentation with Infivit.
Built for the 2026 reality of ML Research & Experimentation: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
Latest research, shipped in 8 weeks.
We track NeurIPS, ICML and arXiv weekly. The state-of-the-art that's actually relevant to you, adapted to your data and live in customer hands inside two months.
Every experiment versioned, end-to-end.
Data, code, weights, environment, seed, all versioned together. Replay any result from any quarter, byte-for-byte, on demand.
Statistical significance, ablations, intervals.
No claim of "improvement" without confidence intervals and ablation studies. Defensible against any technical reviewer, internal, external, or adversarial.
Diffusion, MoE, SSM, GNN, the right tool.
Architecture is downstream of the problem. We pick from the full toolkit, not just the LLM-shaped one currently in fashion. Sometimes the right answer is a 50M-parameter custom model.
No models that "should be better."
Every release fights for its life against a strong baseline. If a new approach doesn't win on stat-significant business metrics, it doesn't make it past staging, full stop.
Your team owns the IP and the methodology.
We don't disappear after deployment. The runbook, eval suite and methodology stay with your team, fully documented, so the next iteration is yours to lead.
The questions you were already going to ask.
Got a ml research & experimentation problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.