The sales pitch for outsourced software usually begins with a spreadsheet. Growth Acceleration Partners begins with a clock. A developer in Costa Rica, Colombia, or another Latin American hub can join a U.S. product team's stand-up in real time, argue about an API before lunch, and push a reviewed fix the same afternoon. There is no overnight relay race and less room for a fuzzy requirement to spend 12 hours becoming the wrong feature.
That is the practical idea behind GAP, the Austin technology-services company founded by Joyce Durst and Brett Bachman in 2007. Today it consults, designs, builds, tests, and modernizes software and data systems. It sells dedicated engineering teams, defined projects, AI and machine-learning work, cloud and data platforms, quality engineering, and product design. Its customers are usually not shopping for an app in a box. They are technology leaders whose roadmaps have outrun their hiring plans, or whose old systems have become too important and too fragile to leave alone.
The firm says more than 600 engineers are deployed across enterprise programs. Its public customer roster includes KFC, Kroger, Microsoft, Amadeus, SolarWinds, Proofpoint, Nissan, Sazerac, MediQuant, and ZenBusiness. The names are broad because the actual product is capacity with memory: a team that learns a client's systems and stays long enough to become useful.
The first betClose enough to disagree before dinner
GAP started more broadly than the company it is now. Early descriptions mixed growth strategy, search marketing, and outsourced development. Durst and Bachman wanted control over the values, processes, and agile methods inside their own firm, while building customer relationships that could survive more than one project. The business was bootstrapped. By 2019, Durst had acquired majority ownership and GAP earned certification as a Women's Business Enterprise.
The focus tightened around engineering. Latin America offered a useful combination: deep technical talent, lower total cost than a comparable U.S. build, and working hours that overlap with North America. GAP organized specialists into Centers of Excellence spanning architecture, cloud and DevOps, data and AI, cybersecurity, quality assurance, front end, back end, mobile, UX, agile delivery, and product work. Engineers receive technical, English, and multicultural training. Employees call themselves “GAPsters,” a nickname just corny enough to suggest it was invented by people who actually use it.
“We don't have the resources or the expertise to do that. Help us build a team and work in a hybrid fashion with our existing technology organization.”Joyce Durst, describing the request GAP often hears from clients
This is where GAP sits in the market. It is more involved than a staffing marketplace and less likely than a giant consultancy to arrive with a small army of slide decks. An embedded team uses the client's GitHub, Jira, Slack, standards, and ceremonies. GAP handles recruiting, payroll, retention, equipment, and delivery leadership. A dedicated squad can range from three people to more than 50; the company advertises an average of roughly 10 business days from an initial strategy session to deployment.
The second betBuy the missing pieces
In 2023, GAP made two moves that explain the company it wants to become. It acquired Mission Data, a Louisville digital-product firm with U.S.-based engineers, UX and product-strategy capability, and experience in retail and connected devices. Mission Data's work included Kroger projects involving freezer and refrigerator temperature sensors and interactive shelving. The acquisition let GAP move further upstream, from supplying engineers to helping decide what should be built, and further downstream into lifecycle support.
GAP also acquired Mobilize.Net's application-migration business. Its lineage stretches back to Costa Rican research on automated code transformation in the early 1990s. The tools translate and restructure legacy Windows and Java applications for modern .NET, web, and cloud environments. GAP later packaged that capability as GAPVelocity AI, a hybrid model in which automation handles repeatable conversion work while engineers make architectural decisions, resolve edge cases, and validate the result.
Long-running nearshore squads add capacity without asking the client to recruit and employ every specialist.
Product strategy, UX, onshore delivery, retail systems, and IoT broaden the work around the code.
Acquired code-conversion tools attack the slowest parts of legacy modernization before humans handle the exceptions.
Prices are negotiated by engagement; there is no public rate card. That matters because “nearshore” is not a single product. A three-person embedded team, an outcome-priced data project, and a legacy migration with proprietary tooling have different risk and economics. The clearest public cost figure comes from a 2026 case study: GAP says a golf-course irrigation modernization used 30 percent fewer engineers, cost 45 percent less, and reached market 20 percent faster than a plan estimated at more than 14 months with 8.5 engineers.
What failed firstThe pilot hit the production wall
GAP's latest change did not begin with a clean victory lap. The company says it spent roughly two and a half years experimenting, failing, learning, and rebuilding its approach to AI-assisted engineering. The recurring failure was familiar across enterprise technology: a clever pilot could generate code, but it lost context, ignored architectural constraints, or collapsed when exposed to real security, testing, and governance requirements. The prompt was not the product.
That changed the firm's idea of an “autonomous engineer.” It is not a bot and not a developer who spends the day accepting autocomplete. GAP describes three levels. At the first, AI produces snippets. At the second, it appears throughout a normal workflow. At the third, agents perform larger blocks of execution while a senior engineer defines the specification, chooses tools, sets guardrails, coordinates the steps, reviews the changes, and remains accountable.
In one legal-data codebase, GAP says a five-agent workflow lifted coverage on a core helpers package from 17 percent to 82 percent in 45 minutes. A reviewer-fixer loop generated tests, found failures, and revised the work before human review. In another engagement, an agent connected to billing systems and shortened a three-day financial close to the same day while producing an audit trail. Across six published engagements, the company reports delivery-time reductions of 30 to 70 percent.
These are company-selected case studies, not a guarantee. They are still useful because they reveal what changed GAP's mind. The value did not come from asking a chatbot to write functions faster. It came from giving models bounded jobs, persistent context, access to the right tools, automated checks, and an experienced person capable of rejecting plausible nonsense. GAP uses Claude Opus for architectural reasoning and orchestration and Sonnet for faster, well-defined execution. The human moves from performer to conductor, but does not leave the concert hall.
The copyable partSteal the operating system, not the slogan
A buyer can use GAP to accelerate a product launch, add a stable squad without a long hiring cycle, modernize a legacy application, build data pipelines, move infrastructure to the cloud, automate testing, or turn an AI prototype into a governed production system. A services founder can copy the logic without copying the company.
A practical five-part playbook
- Sell a collaboration advantage that changes throughput, not merely a cheaper hourly rate.
- Keep teams close to the client's tools, meetings, standards, and business context.
- Automate bounded, testable work first; save ambiguous decisions for experienced humans.
- Build review loops and measurable quality gates before promising dramatic speed.
- Add adjacent capability deliberately, as GAP did with product design and migration tooling.
The model does not work everywhere. It struggles when a client has no engaged product owner, cannot grant safe access to systems and data, or provides requirements so vague that even the humans disagree about success. It also weakens when procurement treats engineers as interchangeable seats and rotates them before they accumulate context. AI makes a confused organization confused at greater speed. A migration tool cannot decide which obsolete business rule should survive.
There are competitive alternatives at every layer: Globant, EPAM, Endava, Thoughtworks, and Publicis Sapient at the digital-engineering end; BairesDev, Gorilla Logic, and FullStack Labs in nearshore talent; specialist modernization vendors; ordinary staffing firms; and the expensive but controllable choice of hiring in-house. GAP's distinction is the bundle: same-time-zone teams, full-lifecycle delivery, acquired modernization IP, and an increasingly explicit AI operating model.
That bundle is credible only as long as the firm resists the easiest AI story. Replacing people is simple to pitch and hard to deliver safely. GAP's more interesting claim is that seniority becomes more valuable when software agents become cheap. The person who can define a system, identify a dangerous shortcut, and take responsibility for production is not overhead. That person is the product.