Company overview
Five years of putting AI where the work actually happens
We are an AI engineering company, not an AI consultancy. Every engagement ends in a system that carries live traffic: a phone line that answers, a queue that clears itself, a platform your team logs into on Monday.
One team covers strategy, design, engineering and the running of what we build. There is no handoff between the people who scope a system and the people who are on call for it, which is why our engagements look longer at the start and shorter at the end.
2020
Operating since
Building production AI systems before the category had a name.
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Systems in production
Kiosks, voice agents and automations carrying real load today.
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Regulated industries
Financial services, healthcare and home services, deliberately.
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Client satisfaction
Across our custom retrieval and voice deployments.
Where this came from
We started in 2020 building AI systems for emergency-room dispatch, a domain where a dropped handoff is not a support ticket. That set the standard for everything since: latency, auditability and fallback behaviour are design constraints, not follow-up work.
Word travelled from healthcare into finance, where the same constraints apply for different reasons, and then into home services, where the phone is the business. Each move was a decision to go deeper rather than wider.
Today the same core team builds voice agents, web and mobile products, process automation and SaaS platforms for teams in banking, home services and healthcare. One team, four service lines, and no handoff between the people who design a system and the people who run it.
Our journey
From one dispatch system to four service lines
Every step here was a decision to go deeper into a domain rather than wider across them.
2020
Founded
Imera AI starts as a small engineering team taking on machine-learning work that had to survive contact with real operations.
2021
First high-stakes deployment
An AI dispatch system goes live in emergency care. Latency budgets, audit trails and human fallback become house rules.
2022
Into regulated finance
The first banking engagements land: document intake, onboarding checks and retrieval assistants built against the institution's own policy library.
2023
Voice becomes a practice
Conversational agents move from a feature we built to a service line of their own, answering live customer calls around the clock.
2024
Platforms, not just point solutions
Clients ask for the system around the model. We add product engineering and SaaS platform work so the automation has somewhere to live.
2025
California focus
We concentrate delivery on the US West Coast, working Pacific hours and naming a market only once the same team can serve it properly.
Today
One team, four service lines
Voice, product, automation and platform work run off a single engineering core across three industries we know well.
Why Imera AI
What changes when the builders are the ones on call
Six things clients tell us are different about working with us. They are all consequences of the same choice: one team, accountable end to end.
We ship systems, not slide decks
Engagements are measured by what is live in production at the end of them. Strategy work exists to get there faster, never as the deliverable.
Regulated-industry fluency
PHI handling, audit trails, retention and human review are scoped in week one because we have been through sign-off in healthcare and finance before.
One accountable team
The engineers who design the system run it. No account layer between you and the people who can actually change the behaviour you are asking about.
Built to be handed over
Your team gets the repository, the runbooks and the model decisions written down. Staying with us should be a choice, not a dependency.
Domain before model
We spend the first weeks on the workflow as it actually runs, including the workarounds people quietly built around the gaps.
On your hours
Delivery runs on Pacific time for US clients, with the engineering team overlapping the working day rather than reporting into it.
How we work
Four phases, and we are still there for the fourth
Most of the risk in an AI project is spent before any code ships. We front-load it on purpose.
Understand
Two to three weeks inside the workflow: call recordings, queue data, the spreadsheet someone maintains by hand. We come back with what is worth automating and what is not.
Design
Architecture, data handling and the failure cases, agreed before anyone writes production code. Compliance review happens here, not at the end.
Build
Short cycles against a running system. You see the thing working on real data early enough to change your mind about it.
Run
We instrument, tune and extend what is live. Accuracy, containment and latency get reviewed on a schedule you can see.
Our principles
Four things we will not trade away
These are not aspirations. They are the reasons engagements with us look the way they do.
Production or it did not happen
A demo proves an idea is possible. We measure ourselves on systems carrying real load, with real users, on a real phone line.
Domain before model
We spend the first weeks on the workflow as it actually runs, including the workarounds people quietly built around the gaps.
Auditable by default
Every automated decision leaves a trail, and every edge case has a route to a human. That is what makes automation survivable in a regulated business.
We stay on it
Launch is the start of the engagement. We instrument, tune and extend the systems we build rather than handing over a repository and leaving.
- Founded
- 2020
- Backing
- US-backed, US-facing
- Engineering
- Gulberg, Islamabad
- Service lines
- Voice, product, automation, SaaS
Available for remote engagements globally.
Five years in, the question is still the same.
Tell us what breaks in your operation today. If it is the kind of problem we build for, we will say so and if it is not, we will say that too.