AI/ML engineering partner
Five practices: safety vision, agentic systems, generative media, film production, and custom ML. Edge inference, on-prem deployment, audit logging, render-farm orchestration — the operational plumbing that lets enterprise teams run AI in production with confidence.
What we actually build
The operator-facing surface of the safety-vision platform. Live camera grid, module health and per-module SLAs, alert routing, and cross-site topology — all driven by the edge-inference and audit-logging stack described on the practice page.
What this looks like for the operations team
See the underlying architecture on the Safety & Computer Vision page, or the cross-site reference architecture on Technology.
Practices
Each practice serves a distinct buyer with its own SLAs and technology stack. The principles that determine whether the work is deployable, however, are consistent across all of them — engineered for production, measurable in outcome, and defined in scope.
Production-grade computer-vision platforms for safety, security, and operational intelligence — deployed on the client's infrastructure.
Read the practice →
Agent runtimes, multi-agent workflows, and MCP-integrated tool use — engineered for enterprises that require them in production.
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Generative-AI tooling that integrates cleanly with existing VFX and animation pipelines — Nuke, Houdini, Maya, Blender — without disrupting the production.
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End-to-end AI-augmented film production across pre-production, production, and post — with IP provenance, talent consent, and audience-integrity guardrails engineered into the pipeline.
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Bespoke ML engineering and applied research — for problems where an off-the-shelf model does not exist, the evaluation criteria do not exist, or the work must begin from the data.
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Lead practice
A multi-site computer-vision platform for Indian schools, hospitals, plants, retail chains, and corporate campuses. Twenty-one production-grade modules. RTSP ingestion, edge GPU inference via Triton + TensorRT, per-site autonomy through WAN outages, WORM audit logging. Per-module accuracy SLAs in contract. DPDP Act 2023 aligned.
Currently in commercial discussion with our first multi-site engagement.
Why us
The engineering choices below compound across multi-year deployments. They are how we build, document, and stand behind the work — not how we market it.
Every project clears a measurable benchmark before deployment. Accuracy SLAs in safety vision. Task-success rates in agentic systems. Supervisor accept-rates in VFX. A proof of concept is not a production deliverable.
What this means in practice: The system that lands is the system that was demonstrated and measured.
Performance commitments live in contract. We measure monthly. Service credits apply when we miss. A benchmark we keep is worth more than a number we'd need to redefine — every practice, every project.
What this means in practice: A monthly report the operations team can act on — not interpret.
On-prem GPU appliances for safety vision — RTSP ingestion, edge inference, WAN-loss failover, fleet rollout across sites. Cloud-burst topology for VFX render and film post. Container deployment, audit logging, and reversible action gates across both. The architecture follows the workload, not vendor preference.
What this means in practice: No vendor lock-in, no surprise cloud bill, no data leaving the network unless contracted.
Each practice publishes the categories we will not build — mass surveillance, non-consensual likeness, undisclosed synthetic content. The exclusion list is contractual, not aspirational. We share it during Discovery.
What this means in practice: Your legal team approves the engagement without redlining the responsible-AI clauses.
How we work
Less ceremony than enterprise consulting; more accountability than vendor-of-record. The day-to-day discipline that turns up at every stage of the work.
The client speaks to the engineer building the system, not an account manager translating between teams. Weekly 30-minute reviews with written notes — no slide decks for engineering work.
One shared repository, one shared channel, one set of dashboards. The client's engineering and operations teams see the same source of truth we do. Architecture decisions land as ADRs in the repo.
Monthly SLA reports on the same date, every month. Misses come with a written root-cause analysis. Service credits apply where contracted — no PR framing in either direction.
Code, model files, runbooks, and configuration live in the client's environment from day one. The maintenance contract carries exit clauses the client can invoke — handover is a documented process, not a renegotiation.
How we engage
Discovery validates the problem and the evaluation criteria. Build delivers the platform. Scale-out proceeds at the client's pace. Maintenance sustains the SLA across the engagement lifecycle. The cadence is consistent across practices; the specific deliverables vary.
Problem scoping, eval design, architecture, integration mapping, and the SLO that decides whether the work shipped. Output is implementation-ready for the next stage — or for any vendor you choose to take it to.
Build the engineering substrate the practice needs — edge inference + Ops Console for safety vision, agent runtime + eval harness for agentic, ComfyUI workflow registry + DCC integrations for VFX — and prove it on one pilot deployment with a post-go-live warranty.
Deploy the platform built in stage 02 across additional sites, shows, agents, or workflows — at the client's pace.
Software updates, model retraining and adapter refresh, per-site or per-show tuning, eval-suite maintenance, business-hours NOC, integration adapter maintenance, quarterly business reviews.
Commercial terms are confirmed during Discovery. We provide honest, bottom-up costing and don't pre-load discount room into our quotes.
Where we are
The platform is engineered. The module library is designed. The accuracy SLAs and cost commitments we will sign to are documented and ready to share during Discovery. We are in active commercial discussion with our first multi-site safety-vision client. AI/ML case studies and named references will be published on this page following each delivered engagement — until then, we present the engineering directly rather than borrow the reputation of other firms.
Interested in becoming our first AI/ML reference deployment? Request a Discovery Call →
Share the operational context, existing infrastructure, and compliance requirements. We respond within one working day.
