80+ products taken from idea to scale

Build them. Build with them.

Agents on the work that's ready. People on the work that matters.

You've seen the demos, and you may have run a pilot that never reached production. We help you deploy agents across business workflows and the SDLC, guided by what your data is ready to support today.

AI-Native Engineering
AI-Native Engineering Graphic

Copilots are step one.

AI-native engineering comes next. Deploy agents across planning, building, testing, and shipping, calibrated to the control your codebase requires.


AGENTIC TRANSFORMATION
Agentic Transformation Graphic

Automate core operations.

Identify high-ROI business processes, build secure custom agents around your existing systems, and hand control to your team to run independently.


THE DIFFERENCE

Know which one you need

AI-Native Engineering

What Changes
How your software gets built
Who it is for
Engineering leaders
Measured in
Speed and stability of delivery
Where it starts
An autonomy map
Method
FINE
Team
FDE
What Changes
Who it is for
Measured in
Where it starts
Method
Team

Agentic Transformation

What Changes
How your business runs
Who it is for
Operations and business leaders
Measured in
Cost, cycle time, and quality of work
Where it starts
An automation blueprint
Method
FAWM
Team
FDT

FAWM decides which agents to build.
FINE decides how we build them.
Need both? Most of our clients end up there.

OUR APPROACH

How we evaluate and deploy AI

We replace "trust us" with clear frameworks at every stage, starting with a short initial step

FINE

FINE graphic

Decide how far agents go in each codebase.

Most teams already have coding assistants. The hard part is knowing what agents can safely own. We map where context lives across your repos, specs, and teams to determine what agents can take on and where humans need to stay in control.

L1
Human-gated
L2
Plan approved
L3
Spec-anchored
L4
Human-gated
How FINE works
→

FAWM

FAWM graphic

Find the work worth handing to agents.

Most agent projects start with a use case and a deadline. FAWM starts with the job. We read your systems and SOPs, sit with the people doing the work, and break it down into jobs an agent could own.


? Every job faces three questions

1 Is it worth doing?
2 Can it be done with the data you have today?
3 Will it pay for itself?
How FAWM works
→

Find your starting point. Leave with a blueprint.

2 days
2 weeks
Timeline Graphic
Building an application?

Connect the Dots

See the end state before you commit. We look at your systems, talk to your people and prototype what you're about to build.

Putting agents into how your business runs?

FAWM discovery

We map the jobs your teams do, score each one, and show which ones agents should own and how much oversight they need.

WHAT WE DO

From the products you build to the systems you run

Agentic Workflows

Runs on FAWM

Pick the workflow that hurts most. Quote-to-cash, incident triage, claims intake. We map it job by job, decide what agents own and what people keep, and build the agents with the approval screens and exception paths your team needs to trust them. Then we measure it against the number you named at the start.

Agentic Workflows Illustration

AI-Native Product Engineering

Runs on FINE

Building a new product or platform? This is where agents can do the most, because the context starts clean. We write the specs first, let agents build against them, and keep people on the decisions that shape the product. You see working software early and often, not a status report.

AI-Native Product Engineering Illustration

Core System Modernization

Runs on FINE

Your oldest systems hold rules nobody wrote down. Rushing agents into that code is how production breaks. So agents read it first. Your engineers correct it. Then changes move faster.

Core System Modernization Illustration
Agentic UX · Runs on both

Agentic UX

We design for both users of your system: the people and the agents.

Agents fail quietly when people can't see what they're doing. And we still design products the way we always have, with research, flows and interfaces people like using.

WHAT WE'VE LEARNED SO FAR

Nobody has this fully figured out.

Here's what we believe after building agents into real systems in energy, hi-tech and pharma.

Keep scrolling
01

A use case isn't a unit of work. A job is.

"Automate customer onboarding" sounds like a plan. It's really forty jobs, and maybe six of them are ready for an agent. Find those six first.

02

Faster isn't the same as steadier.

Coding assistants help teams ship more. Google's 2025 DORA research found they also go hand in hand with more failed changes and rework. Speed without review gates just moves the problem to Friday night.

03

Your data was built for reports, not for agents.

It answers questions after the fact. An agent needs to act on it in the moment. That gap is usually the real project.

04

Some work should stay human.

Regulation, liability and judgment decide that. Enthusiasm doesn't.

CUSTOMER STORIES

Results we're proud of

Agentic Transformation Telecom · Network operations
~50%
targeted reduction in MTTR
Targeted cut in time to resolve, with a root-cause analysis agent for a US broadband provider.
50 percent stat graphic
Agentic Transformation ↗
Telecom
~50%
targeted reduction in MTTR
How agents took over claims intake without losing human review
~50%
targeted reduction
Agentic Transformation
Telecom ↗
How agents took over claims intake without losing human review
Agentic Engineering Law
3X
faster legal research
Building a lending platform with specs first and agents building against them
Structured specs became the contract. Agents built to them, tested against them, and flagged drift — shipping three times faster without sacrificing compliance.
3x stat graphic
Agentic Engineering ↗
Law
3X
faster legal research
Building a lending platform with specs first and agents building against them
3X
faster legal research
Agentic Engineering
Law ↗
Building a lending platform with specs first and agents building against them
Agentic Transformation SecOps
40%
less time on incident triage
Incident triage that routes itself
Agents now read incoming alerts, correlate signals across systems, and assign severity — before a human ever opens a ticket. Teams focus on resolution, not classification.
40 percent graphic
Agentic Transformation ↗
SecOps
40%
less time on incident triage
Fewer SLA breaches, with an orchestration studio for a team supporting 10K+ employees.
40%
less time on
incident triage
Agentic Transformation
SecOps ↗
Fewer SLA breaches, with an orchestration studio for a team supporting 10K+ employees.
Agentic Transformation Pharma
1 week
from kickoff to first release
Instead of ~2 months, with agents that co-author clinical study documents.
Starting from AI-native principles let us move fast without accruing debt. Eight weeks later, the product was live — and the architecture was ready for what came next.
1 week graphic
Agentic Transformation ↗
Pharma
1 week
from kickoff to first release
Instead of ~2 months, with agents that co-author clinical study documents.
1 week
from kickoff to
first release
Agentic Transformation
Pharma ↗
Instead of ~2 months, with agents that co-author clinical study documents.
50 percent
Agentic Transformation Telecom
~50%
targeted reduction in MTTR
How agents took over claims intake without losing human review
We rewired the intake pipeline with specialized agents that classify, validate, and route claims...
~50%
targeted reduction
Agentic Transformation
Telecom ↗
3x
Agentic Engineering Law
3X
faster legal research
Building a lending platform with specs first and agents building against them
Structured specs became the contract. Agents built to them, tested against them...
3X
faster legal research
Agentic Engineering
Law ↗
40 percent
Agentic Transformation SecOps
40%
less time on incident triage
Incident triage that routes itself
Agents now read incoming alerts, correlate signals across systems, and assign severity...
40%
less time on
incident triage
Agentic Transformation
SecOps ↗
1 week
Agentic Transformation Pharma
1 week
from kickoff to first release
A patient scheduling product, built AI-native from day one
Starting from AI-native principles let us move fast without accruing debt. Eight weeks later...
1 week
from kickoff to
first release
Agentic Transformation
Pharma ↗
What are our clients saying?

Our clients love what we do

magicbi logo
cellino logo
Teknor Apex logo
ZUS logo
stratoes logo
Profile image
Hardik Chheda
Founder & CEO
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Autonomous Analytics Platform
We wanted AI to fundamentally change not just how our customers interact with analytics, but how we build the product itself. Working with Zemoso and using Claude Code and MCP has helped us bring that philosophy into our engineering workflow while using Claude to power key intelligence experiences within MagicBI. The result is an AI-native approach to both building and using analytics.
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Ozge Whiting
VP Data & Machine Learning
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Biomanufacturing Tech
I was very impressed with the speed at which Zemoso operated, starting from our first conversational engagement to setting up a team and completing our Design exercise along with a proof of concept to visualize our complex datasets using interactive web technologies. We didn’t hesitate to continue with several development engagements where Zemoso provided a top-notch scrum team to work very closely with our internal teams, always delivering with the mindset of maximum satisfaction. Their understanding of the complexities of an evolving solution and ability to pivot with acute urgency makes them a solid software development partner for any start-up and business out there.
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Fabricio Arteaga
Director of Strategic Relationships and Sustainability
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Global Polymer Innovator
Zemoso's expertise proved fundamental in helping us quickly validate our concept and discover broader market demand than initially anticipated. Their collaborative approach to rapid prototyping and technical assessment not only transformed our concept into a robust, scalable solution but also strengthened TekVentures' own capabilities in venture building.
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Ada Glover
Co-Founder & Chief Product Officer
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Healthcare and Life Sciences
The Zemoso team has been a compelling partner through build and deployment. Initially, the Zemoso team co-facilitated a Design Sprint that resulted in a compelling product that was used for user research and recruitment of early customers. Zemoso then partnered closely with Zus product and engineering counterparts to design, build, test and deploy capabilities on an aggressive timeline. The Zemoso team was collaborative, proactive, and brought a diverse set of capabilities to the table. They continue to be a trusted partner.
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Alok Gupta
SVP Engineering
company logo
Life Science Discovery
The Zemoso team has been super helpful, they have taken a lot of load off the existing team, we were able to churn out a lot more features because of this partnership. Our product team has been able to focus a lot on new customer acquisition and product line diversification. Zemoso development team is an integral part of our team, an extended arm to rely upon.
INDUSTRIES

Deep industry expertise meets intelligent execution.

Oil &Gas
Optimize energy exploration, extraction, and energy distribution networks. Arrow
Oil & Gas Icon
LifeSciences
Build secure, clinical-grade AI platforms and applications. Arrow
Life Sciences Icon
Retail Technology
Deploy AI-powered commerce, supply chain and retail operations. Arrow
Retail Icon
SupplyChain
Optimize logistics, inventory tracking, and end-to-end global distribution networks.
Supply Chain Icon
FinancialServices
Engineer secure, high-throughput platforms for banking, fintech, and digital payments.
Financial Services Icon
InformationSecurity
Implement robust threat detection, data protection, and enterprise-grade compliance architectures.
Information Security Icon
Built on platforms graphic
Zemoso in numbers
100+
Claude certified
architects
80+
Products taken from
idea to scale

Not sure which practice fits?

Neither were most of our clients. Give us thirty minutes and bring one workflow or one system you're worried about. We'll tell you honestly which practice you need, whether you need both, or whether agents belong there yet.

Zemoso Technologies
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