Applied · forward-deployed engineering

AI that earns its place in production.

We embed with your team, learn how the work actually happens, and build systems that run inside the tools and workflows you already depend on.

A live problem, built in your environment, owned in production.

Work with ushello@useyukti.com
Cutaway of a company building. A small cobalt module is lowered into an exact slot while a bulky old machine with tangled cables is wheeled away; one clean blue line now runs through every floor.

01Your existing stack

02The yukti module

03Owned in production

123
  1. 1Your existing stack
  2. 2The yukti module
  3. 3Owned in production
FIG. 01The yukti module, installed
  1. 01Problem
  2. 02Embed
  3. 03Build
  4. 04Production
  5. 05Continue

The thesis

The market has moved past curiosity.

Companies do not need another generic assistant bolted onto the side of the business. They need AI that ships, integrates, and stays useful inside the operating fabric of the company: the tickets, the docs, the tools, the handoffs, the approvals.

That is the bar yukti works to.

Left: the cobalt module sits idle under a glass dome in a museum while people look on. Right: the same module runs inside a busy production line.
FIG. 02Under glass, then in production
A rack with the cobalt module slotted into one bay, cabled to its neighbours.
01

Systems, not demos

Every build has to land inside a real workflow. If it only works in a sandbox, it is not done.

A compact machine: one thin pipe in, one cube out, its dial resting low.
02

Efficient by design

Waste is a product failure. Systems should earn their compute, tokens, and maintenance cost.

A building with one wall cut away, the cobalt module wired into its pipes inside.
03

Embedded, not outsourced

Sit inside the problem with your team. Closer to a senior hire for a stretch than a disappearing vendor.

A running machine with lights on and a cobalt key in its lock, the module on top.
04

Owned in production

Success is a system that runs with clear ownership, monitoring, and room to change.

The workhorse test

Not everyone needs a tank. Everyone deserves a good workhorse.

Most company problems do not need the biggest model or the most elaborate system. They need something dependable that does the job every day, inside the workflow, at a cost that makes sense.

That is what Applied builds.

A plain hand-cart carrying the yukti module along a cobalt lane, while a large tank-like machine sits unused behind a fence.
FIG. 03The workhorse and the tank

Applied

What we take on

Four kinds of work. One delivery posture: forward-deployed, production-bound.

Two people at a wall of sticky notes; most are crossed out, one cobalt note moves into its own column.
01

Decide what deserves to ship

Not every AI idea should be built. We pressure-test ownership, definition of done, stack fit, failure modes, and whether the return is worth the cost.

  • Ranked problem

    Or a clear no.

  • Path to production

    Only if yes.

  • Commercial shape

    Tied to that scope.

A row of desks joined by a small conveyor; an engineer connects the cobalt module and trays move smoothly to every desk.
02

Systems inside operations

Production systems that remove friction from work that already exists: handoffs, reviews, intake, fragile internal tools.

  • Workflow systems

    Move work without constant babysitting.

  • Integrations

    Across platforms you already pay for.

  • Monitoring

    Know when quality or cost drifts.

A designer places a cobalt panel into a large product interface while a colleague tests on a phone.
03

Product surfaces that use models

When AI belongs in the product itself. Model-backed features inside software customers or operators already use.

  • In-product features

    Not a parallel prototype app.

  • Evaluation first

    Before broad rollout.

  • Same builders

    Iterate after launch.

One person watches a chart held inside a tolerance band while another checks the module in its rack with a clipboard.
04

Make it reliable enough to keep

Shipping is not the finish line. Structure around quality, human control, regressions, and change without a rewrite.

  • Acceptance criteria

    Tied to business outcome.

  • Review loops

    Before wider release.

  • Owned handoff

    Not only code.

Process

How we work

From problem to production, with an explicit output at each step.

  1. 01
    The engineer and the client talk across a small table, a magnifying glass over one box between them.
    Problem

    Concrete use case with an owner. Hard questions early.

    Out: go / no-go + scope
  2. 02
    The engineer works at a desk among the client team, a kolam at the doorway.
    Embed

    Engineer joins your team and sits in the workflow.

    Out: shared workflow map
  3. 03
    The engineer assembles the cobalt module at a workbench beside the client racks.
    Build

    In your environment, against your stack, early working slices.

    Out: system on the real path
  4. 04
    The module runs in the rack; the engineer hands the client a key.
    Production

    Live where work happens. Ownership, watch, reverse, improve.

    Out: live system
  5. 05
    Engineer and client review a chart together, the next module already on the desk.
    Continue

    Optional retainer. Same people. No relearning from zero.

    Out: continuity
The engineer and a client talk over chai at a round table, a clock showing a short half-hour slot, a kolam at the entrance.

Engagement

How it starts

  • Starts from a problem, not a package
  • Short first conversation
  • Proposal follows the problem
  • Direct access to builders
  • Progress is what is live
The engineer slides the cobalt module into an exact slot while the owner holds the key; a blank presentation easel and a round ball sit ignored to either side.

Fit

Who this is for

Good fit: live workflow, clear owner, production intent, willing to embed.

Bad fit: deck-only goals, generic chatbot with no workflow change, staff-aug benches more than a build team.

FAQ

Questions

01What is Applied?

yukti is an AI research lab. Applied is its forward-deployed engineering arm: we embed with companies to put AI systems into production.

02How is Applied different from a typical agency?

No packages, long discovery theater, or layers of account management. A forward-deployed engineer works inside your problem and ships into your environment.

03What does forward-deployed mean?

Someone from yukti joins your team for the engagement: learns the workflow, builds, integrates, and stays through production.

04What kinds of problems do you take?

Operational systems that need to run every day, and product surfaces that need model-backed features to ship and hold up.

05How long do engagements take?

Depends on scope. Narrow systems can move in weeks. Larger surfaces take longer, with working slices early. Honest range after the first call.

06How do you price?

From the problem. After a short call and clear scope, we propose terms. Bad fit on budget or timeline: we say so early.

07Ongoing support after launch?

Yes. Many engagements move into a retainer with the same people for iteration, monitoring, and adjacent builds.

08NDA and work inside our systems?

Yes when required. Access follows least privilege and your security process.

09How do we start?

Email hello@useyukti.com with the problem and who owns it. Call, then proposal if fit.

Contact

Bring the problem.

Tell us what has to work in production.

hello@useyukti.com

We will say yes, no, or not yet.

A client carries a crate of tangled cables to an open door where the engineer reaches out to take it, a kolam on the threshold.