Agent platform for BCCL, The Times of India Group
Custom agents and vendor-agnostic orchestration for newsroom and business workflows. Built in their infrastructure, owned by their team.
We build the systems everyone else calls too ambitious.
Base42 is an applied AI lab. We take on the builds at the top of a CXO's wishlist: closed-loop intelligence for precision plants, agent platforms that run enterprise workflows. Engineered to production, on a stack you own outright.
trusted by teams at
- The Times of India BCCL Group
- Delhivery
- Blue Tokai
- Eggoz
Most enterprise AI dies between the demo and the deploy.
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
You have four ways to get this built.
Most vendors pretend alternatives don't exist. You'll evaluate them anyway, so here's the honest comparison.
Hire an in-house AI team
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.
A model vendor's services team
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.
Off-the-shelf AI tools
The job is generic: meeting notes, support macros, boilerplate drafts.
Your most valuable workflows are not generic. That is precisely why they are valuable.
A large consultancy
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.
What we build
Three kinds of work, one common thread: the problems other shops quote around.
agents/
Custom agents and harnesses
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.
orchestration/
Model-agnostic orchestration
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.
research/
Applied research and reinforcement learning
When the problem needs more than prompting: reinforcement learning for industrial control, evals, fine-tuning on your data. The deep end, where wrapper shops decline to quote.
Current work
Closed-loop intelligence for precision manufacturing
One system closing the loop for a precision plant: sensing, decisioning, control, with reinforcement learning driving efficiency where tolerances are the whole business.
Detailed walkthroughs of either engagement on the call.
How we work
Senior engineers only
Nobody learns on your budget. The people on the call are the people writing the code.
Production or nothing
A POC is a milestone, never the deliverable. Done means running your real workflow.
You own the stack
Code, prompts, weights, evals, docs: all transfer to you. No black boxes, no dependency by design.
Model-agnostic by architecture
Best model for each job, proven with evals, re-picked when the market moves. It will move.
Bring us the problem everyone else quoted around.
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.