The form was already digital. That was part of the joke. At a UK healthcare field-services business, surveyors still encountered weak connections, returned to paper, and entered information again later. The company had software. What it needed was a working day with less typing.
- Matellio builds custom software and upgrades systems companies already run.
- Its work crosses AI, cloud infrastructure, connected devices, and business applications.
- The practical test: can a specific workflow become faster without creating another handoff?
Matellio’s response was to extend the customer’s existing operations platform. Surveyors could record observations by voice, save inputs locally while offline, and synchronize when connectivity returned. Amazon Transcribe converted recordings to text; Claude through Amazon Bedrock helped place observations into the correct fields. A surveyor reviewed the result before submission.
The useful detail is that last step. The person doing the work remained in the loop. The machine took a pass at the paperwork. This is an unusually clear introduction to a company whose services otherwise threaten to become an alphabet soup of AI, IoT, APIs, and cloud infrastructure.
A small failure, followed all the way through
The survey form was only half the job. Back at the office, employees compared updated survey information with the original contract, identified additions and substitutions, calculated their financial effect, and notified colleagues. Matellio added a second capability to analyze those changes and produce a structured summary inside the same platform.
Its published case estimates that form completion fell from 45-60 minutes to 10-15 minutes, while contract review dropped from 20-30 minutes to under two. Those are vendor estimates. They describe the attraction of removing repeated work, rather than a guarantee for the next customer.
The company estimates monthly AWS usage at $100-$150 and operational savings at £1,500-£2,250, assuming roughly 20 surveys a week. Keep those numbers in separate drawers: cloud consumption is a running expense, not the software development invoice. A return calculation also needs implementation, support, review time, and the work required to keep the system accurate.
The advantage of having been there before
Why Matellio? In this case, familiarity mattered. The studio had already delivered a CRM transformation and built the platform being extended. It knew the customer’s workflows. The follow-on sale began with knowledge another supplier would first have to acquire.
That pattern appears in its workforce-management case. Matellio had built the foundational platform before adding a multilingual HR chatbot. Routine questions about timesheets, leave, and policy had been consuming human support capacity. The new assistant answered inside the application, using live records or company-specific documents according to the question.
The interesting engineering sits beneath the conversation. Employees should see their own records; managers need a different scope. Documents for one company must remain separate from another’s. Matellio describes support for more than 18 languages across more than 20 company profiles. Access rules are part of the product, as essential as the fluent reply.
“job number one is sense-making”Amrit Singh, Chief Strategy Officer
May 2024 partnership announcement
A studio with a long service corridor
Founded in 2014, Matellio traces its beginning to four engineers. Its June 2022 leadership announcement identifies Apoorv Gehlot, Dilip Singh, Puneet Rao, and Dr Deepak Singh Gehlot among its founders. Gehlot became CEO; Rao became Global COO. Today its pitch connects product engineering, workflow modernization, and the infrastructure needed to keep software operating.
Customers can commission an MVP, a mobile application, a legacy-system overhaul, a cloud migration, or an AI feature. The market position is that of an outside engineering partner with several disciplines available together. A customer buys a team’s ability to design, integrate, test, and maintain a particular system.
Its industrial work makes that breadth concrete. For Spectralink, Matellio describes firmware improvements, compatibility across Cisco, Avaya, and Mitel phone systems, and automated testing. For Inseego, it describes rebuilding fleet-tracking data pipelines and compliance workflows. A handset release and a survey form look unrelated until both depend on fixing the machinery behind the screen.
A legal-technology deployment follows the same pattern. Matellio had already worked on the trademark platform before adding an engine to search existing marks and prepare structured risk assessments. Attorneys then reviewed and finalized the generated reports. The company reports a 40-50% reduction in attorney review time, measured against earlier work. The distinction matters: a shorter review is a productivity result. It does not, by itself, establish that every risk assessment is correct. A buyer still needs to inspect the output and decide what evidence would justify trusting it.
The money buys a continuing job
The business model combines project work with continuing operations. Its AI offer begins with a free scoping call and a paid three-week discovery sprint, credited toward implementation if the customer proceeds. Cloud support can become a monthly retainer; managed AI operations add monitoring and refinement after deployment.
There is also a strategic wrapper. A May 2024 alliance with New Port Partners pairs business transformation advice with engineering delivery. Matellio announced AWS Advanced Tier status in October 2024 and, on October 1, 2026, AWS AI Competency in Generative AI. Those milestones describe its growing cloud specialization.
The continuing work has boundaries. Matellio’s managed AWS service describes infrastructure monitoring, patching, backup checks, recovery runbooks, and cost reviews. Application feature development sits outside that operating scope. This is useful contractual detail: keeping a cloud environment healthy and changing the product are different assignments. For a small company without its own operations team, buying that capability can make sense. The customer still needs someone responsible for product decisions, business priorities, and approving changes. An outside team can maintain the machinery; it cannot infer every commercial choice.
Buy the workflow. Specify the finish line.
One customer, social platform ISIT, told Clutch it hired Matellio after an earlier developer’s platform failed to function. It reported spending roughly $120,000 on the rebuild. A different reviewer described canceling after a development estimate increased substantially. These are individual accounts, but together they make scope and acceptance tests worth discussing before the first sprint.
The copyable lesson is practical: identify the repeated task, observe where information changes hands, establish a baseline, and decide who approves the result. A standard workflow already served by packaged software may not justify custom engineering. Unclear ownership or unreliable data can also swallow the expected savings.
Matellio’s most persuasive cases start with a bounded inconvenience and follow it through the business. The surveyor speaks. The form fills. A human checks it. The office receives something it can use. That is a finish line a buyer can recognize.