Consciera
Consciera · AI systems built around how you work

Your AI thinking partner, built around how you actually work.

We find the one thing in your business AI should actually be doing, build it into the tools you already use, and hand it over working. You stay in the loop where it matters — because a system you can’t check isn’t one you can rely on.

See what’s worth buildingWatch us build one

or email hello@consciera.ai

What we build

A thinking partner, not an employee.

Most of what’s being sold right now runs on the employee model: hand it the job, let it work, read the summary afterwards. That model rests on one assumption — that the system’s account of what it did is a record of what it did.

It isn’t.

These systems produce fluent, specific, entirely sincere explanations of results they reached some other way. That isn’t a bug anyone is going to patch. It’s how they work, and it’s what we’ve spent months demonstrating in public with the sources on screen.

So we build the other kind.

The employee model

Delegate, then read the report

Runs unattended. Reports done. You spot-check occasionally and trust the summary — which works until the summary is confident about something it had no way to know.

What we build

A partner you stay in the loop with

Built around the decisions that actually matter, with a person at each one, and a check in front of every output somebody would otherwise act on without verifying.

What you get

A system that’s running, not a plan for one.

How it runs

Three stages, and the first one can end it.

  1. 01

    We find out what's actually broken

    Before anything gets built. Interviews with the people doing the work, and a written answer to what should be automated, what shouldn't, and what would go wrong if it were.

  2. 02

    Scope and price, in writing

    Fixed before the build starts. If the first stage says you don't need this, it says that — and you've bought a defensible answer instead of a system you'd have regretted.

  3. 03

    We build it

    In your environment, with you watching it get made, so it isn't a black box the moment we hand it over.

What it costs

Scoped, and the first stage comes off the rest.

Scoped to the work

The build

Quoted

The first stage — what's broken, what to build, what not to — is priced on its own and credited in full against the build if you go ahead. Both numbers come back within a few days of talking, in writing, before anything starts.

If you're the whole business, that's usually one thing doing the job you keep pushing to the evening. If there's a team, it's more often a set of workflows that hand off to each other. Either way we'll tell you which after the first stage, not before it.

Want your team to understand this rather than have something built? That’s a live session — a different thing, priced separately, and nothing credits between them.

Where it starts

Tell us what’s slow.

We’ll come back with a first read on whether AI should be doing it, what it would take, and what it would cost — or that it isn’t worth building, which is also an answer.

No obligation and nothing automated — a person reads it and replies. Your details are used for this conversation only.

Who builds it

Suma Gowda

Software Engineer and AI Technical LeadMS Computer Science, USC · Los Angeles

I have architected AI systems that went into production: retrieval over vector search, autonomous multi-step agents, and a governed layer controlling what data a model is allowed to touch — along with agent-driven workflow automation running against real users and real data.

  • 20 years building software; 15+ products from zero to launch
  • Former CTO and founding engineer of a HIPAA-compliant health platform — patented technology, taken to $1M+ annual revenue
  • Global Talent Endorsement for technical contribution, UK and Australia
  • Contributor to Amazon’s open-source AWS Lambda Rust runtime
  • Every session this thinking comes from is public and uncut.
Suma Gowda

Before you ask

Questions we get.

How is this different from the people selling “AI employees”?

They build the thing. We build the thing and then tell you which parts of what it says you can act on.

That sounds like a small difference until something it sounded certain about turns out to be wrong in front of a customer. The employee model assumes a system's report of its own work is reliable. That assumption is exactly what our public sessions take apart, with sources.

We already have someone building AI things internally.

Then you probably don't need the build. What teams in that position usually want is a review — someone from outside going through what's already running and naming where it produces confident output nobody can verify.

That's the first stage on its own. Say so when you write and we'll scope it that way.

What if the first stage says we shouldn't build anything?

Then it says that, in writing, and you've spent the smaller number to avoid spending the larger one. That's a real outcome and we won't manufacture a project to avoid it.

How long does a build take?

Depends on what it is, which is why the first stage exists. Most useful systems are smaller than people expect — one workflow done properly beats a platform nobody trusts. We'd rather ship something that works in three weeks than something ambitious in six months.

Will we be locked into you afterwards?

No. The handover is written for whoever maintains it, and we build in your environment with you watching for exactly that reason. Ongoing support exists if you want it and it's optional — a system you can't run without us is a worse system.

Do we need someone technical on our side?

No. You need to know your own work — what's slow, what's error-prone, what a good outcome looks like. That's the part we can't supply and you already have.

The technical judgement is what you're paying us for, and we don't hand it back at the end. The system runs without us, the handover is written for whoever keeps it going, and if that person doesn't exist yet we'll tell you what the job actually involves before you hire for it.

Have you done this for companies like ours?

This is newly offered and we're not going to invent a client list. What exists instead is public: every session uncut with the sources on screen, and the production work described above.

If that isn't enough evidence for the size of decision you're making, that's a fair call, and we'd rather you made it now than later.

Can you work with our stack?

Tell us what it is when you write. Most of this work has been on large models with retrieval, agent workflows and a governed layer between the model and the data it's allowed to touch — but the question that matters is what your systems already do, not which model sits underneath.

Watch one before you decide.

A new session goes out live every week and stays up uncut, with the sources on screen. It’s a better way to judge how we think than anything we could write here.

Get a scope and a priceWatch a session

or email hello@consciera.ai