A customer once worried that Azumo wasn’t billing enough. Other contractors were logging hours. Azumo’s developer seemed suspiciously quiet. In founder Chike Agbai’s telling, the explanation was simple: a machine-learning model was training on a GPU. The developer monitored it. Charging for sitting beside it would have made the timesheet more impressive and the software no better.
It is a useful little story about a business that sells engineering. Buyers can count people, hours and meetings. Working software is harder to judge. Azumo’s proposition lives in that gap: hire a team that understands the result you need, then give it the engineering work required to get there.
- Latin American engineering teams working overlapping U.S. hours.
- Custom AI, applications, data pipelines and cloud operations.
- Client work includes Meta supplier search and Discovery voice trivia.
- Its own products include Valkyrie, Charlibot and Hello.
The engineer shortage, seen from the deal table
Before starting Azumo in 2016, Agbai advised technology companies as an investment banker. He kept encountering businesses that could sell sophisticated software yet struggled to find the people to build and maintain it. If large technology companies had that problem, he reasoned, smaller businesses probably did too.
His answer was a distributed development company. Azumo recruits in Latin America and works alongside customer teams during their business day. The advantage is easy to picture: a question asked at a morning standup can become an afternoon decision. That matters when requirements keep moving and nobody wants to spend a day waiting for a clarification.
The founder’s letter argues for developer careers, learning and room to act independently; it also says everyone can earn a company stake. Those commitments help explain the intended relationship with clients. Azumo wants to supply a continuing engineering organization, with shared context, rather than make each hire a fresh transaction.
A database full of invisible suppliers
Consider its work for Facebook, now Meta. At conferences, supplier information could disappear into business cards and scattered records. Azumo built a conversational registration application that collected details and organized suppliers. Its account puts delivery at eight weeks.
A subsequent search engagement confronted a different problem. Meta had more than 3.5 million supplier records, but traditional search struggled to retrieve the right businesses. The missing ingredient was a useful relationship between the words in a query and the capabilities in a supplier’s description.
Azumo’s published technical account describes cleaning supplier data, extracting entities and using FastText representations to find domain similarities. It reports a search-precision improvement above 40 percent. The transferable idea is modest and valuable: before buying more information, make the information you already own easier to find.
Relevant matches depend on how the data is represented.
Make the editor independent
For Discovery Channel’s Latin American audience, the job was more playful. Quizcovery let viewers answer trivia questions through Alexa and Google Assistant. The difficult parts included Spanish-language understanding, regional variations and keeping multiple versions current.
Azumo used its conversational engine to let Discovery update content without returning to the development team. One editorial change could reach multiple versions. That detail deserves attention. A voice interface is entertaining on launch day; an editor who can maintain it independently makes it a usable publishing tool.
“The work was highly complicated and required a lot of planning, engineering, and customization.”
Costa Constantinou · Discovery Channel
The same engineering-before-embellishment pattern appears in its Sparks & Honey engagement. Azumo stabilized the existing Q cultural-intelligence application, improved ingestion and separated frontend from backend before extending generative AI into briefings and reports. The architecture needed attention before the new features could comfortably move in.
The price of a finished piece
Azumo sells staff augmentation, dedicated teams and project delivery. Its expertise spans applications, data engineering, AI and cloud infrastructure. A customer might need another developer, a team to modernize a system, or engineers to carry a model into production. These are different purchases.
Its current custom-development page lists $899, $699 and $499 per “Delivery Unit” across Starter, Growth and Platform tiers. A unit is a defined, testable piece of software with acceptance criteria, graded for complexity. Those figures are unit prices. The total depends on the agreed scope. The founder’s letter also describes hourly billing, so buyers should settle the engagement model explicitly.
- 01 Define the scope
- 02 Agree what passes
- 03 Grade the complexity
- 04 Price, build, verify
Its AI service describes a model-independent approach, selecting among commercial providers and open-weight alternatives, with deployment on AWS, Azure or Google Cloud. For a buyer, that widens the discussion beyond the chatbot window: the same engagement may need retrieval, backend integration, monitoring and a way to release changes safely. The chosen model sits inside that larger system.
In August 2023, Azumo announced SOC 2 Type I compliance after an audit by Prescient Assurance. For customers handing an outside team access to business data, security controls belong in the purchasing conversation alongside technical skills and delivery schedules.
That places Azumo between freelance hiring and larger consultancies. Its particular combination is regional proximity, an ongoing engineering team and AI experience alongside ordinary application work. The practical buyer still needs to specify an outcome and appoint someone who can accept the result. Overlapping calendars cannot repair an undefined brief.
The phone rings for the builder
Azumo also uses its own engineering. Agbai described replacing a $700-a-month answering service with Charli, an agent connected to a directory, Slack and the phone system. He reported recurring savings above 90 percent, a comparison of recurring service costs. During deployment, three webhooks still pointed at a dead development address. Call logs exposed the DNS errors. Updating the endpoints fixed them.
There is a useful instruction hiding there: test the whole handoff after deployment. A fluent conversation is only one part of answering a call.
The product lineup now includes Hello for business calls and Charlibot for conversational workflows. Valkyrie tackles another operational task: fine-tuning and serving open-weight models on dedicated hardware. Customers reserve concurrent agents; excess work queues. Model versions remain pinned until the customer changes them. Predictability comes with a capacity decision.
Across these offers, the useful question stays remarkably ordinary. Can someone find the supplier, update the quiz, take the call or deploy the model? Azumo’s most persuasive work gives the person doing that job a shorter route through it.