i.
Evals before agents.
We start every engagement with a measurement system. Golden datasets, LLM-as-judge pipelines, and CI gates — so quality is something the team can defend, not just demo.
Now accepting Q3 engagements
The variable you can count on
Covariate Labs is a forward-deployed AI studio. We embed with product teams to ship production agents, eval pipelines, and data systems — the work most agencies hand-wave through.
- 6wPilot to production
- 2Production AI products live
- Forward-deployed · founder-led
- Amazon · Los Alamos · NIST
01 / Approach
Most AI work fails in the same three places.
A demo isn't a system. By the time most AI projects reach production, the prototype that won the bake-off is a tangle of brittle prompts, untested edges, and silent regressions. We work on the parts that decide whether a system survives its first real users — the unglamorous infrastructure that turns a model into a product.
ii.
Agents that survive contact.
Production-grade agents and copilots built on LangGraph, MCP, and the Vercel AI SDK — with tool boundaries, retries, fallbacks, and human-in-the-loop checkpoints designed in, not bolted on.
iii.
The product around the model.
Full-stack delivery — Next.js, React Native, Python, AWS — so the agent ships inside a real product surface with auth, billing, observability, and the ops to keep running.
02 / Products
Two products, built to ship.
Verticalized AI surfaces we're investing in alongside client work. Both are production systems with peer-reviewed research behind them — not demos, not vaporware.
Product 01
KilnSight
Energy & Industrial
Digital-twin and reinforcement-learning control for industrial wood drying.
43%Site energy reduced
~8dDrying time shortened
94%Carbon intensity drop
KilnSight optimizes industrial wood drying for any species and kiln configuration. A high-fidelity digital twin captures the coupled heat and mass transfer between the heat pump, kiln chamber, and wood moisture-stress behavior — and a multi-agent reinforcement learning policy generates drying schedules that adapt in real time to sensor data.
What it delivers
- Optimal drying schedules per species and kiln
- Real-time adaptation to live sensor telemetry
- Reduced cracking, warping, and quality defects
- Distilled heuristic policies for production deployment
Peer-reviewed · DOE-funded research
Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systems
Bhatta, Waseem, Liu, Yang, O'Neill, Chang · Drying Technology · 2026
DOE DE-EE0010201
Read paperFor sawmills, lumber producers, kiln OEMs
Product 02
MetaAnnotate
AI Data Infrastructure
AI-automated data annotation with human-in-the-loop validation and structured metadata.
- 01Schema
- 02Pre-annotate
- 03Human review
- 04Metadata
MetaAnnotate combines AI automation with structured human review to deliver production-grade datasets. Point it at a corpus and a task; it generates the annotation schema, runs AI pre-annotations at scale, routes work to reviewers through guided UIs, and ships out labeled data with full metadata — context, confidence, and traceability for every example.
What it delivers
- Auto-generated annotation schemas from task description
- AI pre-annotations to slash reviewer workload
- Structured reviewer workflows with confidence scoring
- Per-example metadata: context, confidence, lineage
Use cases
- Eval datasets for production agents
- RLHF and SFT datasets for fine-tuning
- Document classification & extraction
- Vertical-specific labeling pipelines
Currently in private beta
03 / Engagements
Three ways to engage.
From a tightly scoped pilot to a long-running embedded pod. Pricing is fixed in writing before kickoff; no hourly meters, no scope drift.
Tier 013–4 weeks
Pilot
One workflow, one agent, one shipped surface. For teams who want to see what a production-grade build actually looks like before committing further.
- Discovery + scope, fixed in writing
- Single production-grade agent or workflow
- Eval suite + golden dataset (~200 cases)
- Deployed to your infrastructure, your repo
$25–40kfixed
Tier 02 · most common6–10 weeks
Build
A complete production system. Multi-agent orchestration, integrations, the application surface around it, and the observability to run it. Most engagements start here.
- Multi-agent system with tool boundaries
- Full-stack product surface (Next.js / RN)
- Observability, evals, regression CI
- 30 days post-launch hardening included
$60–150kfixed
Tier 036+ months
Embedded
A dedicated pod sitting alongside your team. For companies who've found product-market fit and need the AI surface to keep evolving as quickly as the rest of the product.
- Dedicated 2–4 person engineering pod
- Ongoing eval tuning + model upgrades
- Founder-led architecture review monthly
- Roadmap-aligned shipping cadence
From $18k/ month
Every engagement begins with a 30–minute scoping call. If we're a fit, you receive a written proposal within 48 hours: scope, milestones, fixed fee, and the names of the engineers who will work on it. No multi-stage sales process, no senior-bait-and-junior-switch.
04 / Research
Backed by peer-reviewed research.
Every product we ship is grounded in peer-reviewed publications — applied machine learning for the systems and physical processes our clients actually run.
- 2026Pretrained LLMs as real-time controllers for robot-operated serial production linesMachine Learning: Engineering
- 2026Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systemsDrying Technology
- 2025Demand-driven hierarchical integrated planning-scheduling control for a mobile robot-operated flexible smart manufacturing systemRobotics & Computer-Integrated Manufacturing
- 2025Train small, deploy large: scaling multi-agent reinforcement learning for multi-stage manufacturing linesJournal of Manufacturing Systems
- 2025Enhancing production in robot-enabled manufacturing systems: a dynamic model and moving horizon control strategy for mobile robot assignmentJournal of Manufacturing Science & Engineering
05 / FOUNDERFounder-led,
Founder-led,
every engagement.
The person who scopes the work is the person who reviews the architecture and signs off on every milestone. No senior bait, no junior switch.
Kshitij founded Covariate Labs to bring research-grade ML engineering to teams shipping production AI — the unglamorous infrastructure between a model that demos and a system that survives its first real users.
The work draws on a decade of applied ML research for production environments — turning peer-reviewed research into systems that ship, instead of demos that get shelved.
Covariate Labs is the place that engineering bench ships from. Engagements are deliberately limited — the same person who scopes the work reviews the architecture and signs off on every milestone.
Cadence
Founder-led
architecture review
architecture review
Roster
2–3 concurrent
engagements, max
engagements, max
Method
Applied ML for
physical systems
physical systems
Things buyers ask early
07 / Start a conversation
Your outcome has a missing variable.
A 30-minute call to scope the problem, talk through what a realistic engagement looks like, and decide whether we're the right team for it. No deck, no follow-up sequence.
08 / Send a message
Send a message.
For questions, intros, or anything that doesn't need a calendar slot. Attach RFPs, briefs, or sample data — we'll read it before we reply.