Base42 is an applied AI lab. We take on the builds at the top of a CXO's wishlist: a governed AI platform serving 5,000+ employees at one of India's largest media groups; machine intelligence for precision manufacturing. Engineered to production, on a stack you own outright.
A demo takes a weekend. A system that runs your real workflow, with your data, your edge cases, your uptime, is a different discipline. That gap is where pilots stall and budgets die.
Closing that gap is the only thing we do.
spec·sheet — build options, compared honestly
Most vendors pretend alternatives don't exist. You'll evaluate them anyway, so here's the honest comparison.
You have time, and a brand senior ML engineers want to join.
6 to 12 months of recruiting, and then you still start from a blank page.
Your problem matches their model's sweet spot, today and next year.
They sell their model, not your outcome. Their architecture hard-wires you to their pricing.
The job is generic: meeting notes, support macros, boilerplate drafts.
Your most valuable workflows are not generic. That is precisely why they are valuable.
You need process, headcount, and paperwork at global scale.
Partners sell the deal, juniors build it, and you get a deck about a POC.
Where Base42 fits: a small senior team that ships the system, then hands you the keys. Code, prompts, evals, docs: all yours, built so your engineers can run it without us.
Four kinds of work, one common thread: the problems other shops quote around.
An AI workspace your legal, finance and HR teams are actually allowed to use: corporate SSO, role-based access, encryption at rest, an audit trail on every run, PII detection, and prompts that never leave your boundary or train anyone's model. In production today at a national media group.
Agent systems built for one job and finished to production: sales decks assembled from your CRM, pipelines that draft, check, and file. Boring to operate. Boring is the compliment.
Models as swappable parts: each task routed to whichever of Claude, GPT, or open weights does it best, with evals to prove it. When the market moves, you change a config, not your architecture.
When the problem needs more than prompting: physics-informed features, models that learn a machine's normal operating envelope from telemetry alone, digital-twin residuals, and the closed loop that turns them into corrections. The deep end, where wrapper shops decline to quote.
A private AI workspace for the group, engineered around privacy, governance and security: SSO and role-based access, an audit trail on every run, encryption at rest, and a model-agnostic runtime that reads the spreadsheet, runs the Python, and returns the deck. 5,000+ employees, over a billion tokens a week. Built in their infrastructure, owned by their team.
Machine intelligence for a precision parts manufacturer: first a measurable baseline for how accurately the plan survives contact with the machine, then a model of the machine environment learned from controller telemetry, CAM programs, tooling and material. The direction is a closed loop — plan, machine, observe, correct.
Where the media-group build goes next: Microsoft 365 as a first-class surface, a builder for team-specific agents, department knowledge bases and shared templates — so the platform stops being a place people visit and becomes where the work already happens.
Detailed walkthroughs of any of these on the call.
Nobody learns on your budget. The people on the call are the people writing the code.
A POC is a milestone, never the deliverable. Done means running your real workflow.
Code, prompts, weights, evals, docs: all transfer to you. No black boxes, no dependency by design.
Auth, access control, audit and data residency are designed in, not retrofitted. Retrofitting governance is a rewrite.
Best model for each job, proven with evals, re-picked when the market moves. It will move.
Base42 Systems is an applied AI lab. It engineers production-grade AI systems — governed enterprise AI platforms, custom agents, vendor-agnostic orchestration, and machine intelligence for industrial processes — for problems off-the-shelf tools cannot solve and in-house teams cannot hire for quickly.
Yes. Code, prompts, weights, evals and docs transfer to the client. The architecture is vendor-agnostic so models can be swapped without a rewrite.
A model vendor sells its model. Base42 sells the production outcome on whichever model wins the eval for each job, this year and next.
The governed enterprise AI platform for BCCL, The Times of India Group, is in production for 5,000+ employees and processes over a billion tokens a week. A contracted engagement with ShiaanX covers machine intelligence for CNC precision manufacturing. Walkthroughs of either on the call.
Thirty minutes with the founder. You describe the problem; we tell you straight whether it's buildable, what it takes, and whether we're the right people. Sometimes the answer is no. That's how you know the yes means something.