Senior engineering capacity,
built on the same AI-native patterns
we use to build our own systems.

Engineering capacity that ships, not staffing.

You are not buying time-and-materials labor. You are buying outcomes. We bring a senior team that's already fluent in the patterns AI engineering actually uses today: agent orchestration, MCP-based tool integration, human-in-the-loop review cycles, eval-driven development. The patterns we use to ship Qandaba OS internally are the patterns we apply to your codebase.

Working software every sprint. Weekly demos to your stakeholders. Code that reads like your team wrote it, designed to be handed off and extended without us.

The work this team does well.

• AI-bearing product features (search, summarization, agentic flows, copilot surfaces)
• Internal agent workflows (operations automation, data extraction, document handling)
• Internal tools and platform work that touches AI infrastructure
• Greenfield AI-native applications on a tight timeline
• Rescue work on AI projects that stalled and need senior judgment

Companies who need senior capacity without growing the bench.

Most engineering orgs don't have 3 to 5 senior AI-native engineers sitting unused. Hiring them takes 9 to 12 months and isn't always the right structural answer. We come in as the bench you'd want to build, ship the work, and either hand it off cleanly or stay on retainer if the pipeline keeps producing AI-bearing work.

What every engagement includes.

• A senior team scoped to the work, not a bench of mixed levels
• Weekly demos, written status, and a working software increment every sprint
• A practitioner from Qandaba in every standup, design review, and demo
• Code reviewed against the same standards we apply to our internal Company OS
• Clean handoff documentation if the engagement ends, including the parts that are typically left out (eval data, prompt history, agent configurations)

Two shapes, depending on how steady the demand is.

Project. A defined scope, a fixed window, a working artifact at the end. Best for companies with a specific AI-bearing feature or system to build, where the question is "ship this thing" rather than "extend our capacity."

Retainer. A senior team allocated to your priorities every sprint, refreshed as work completes. Best for companies with a continuous pipeline of AI work where the bottleneck is senior judgment, not hours.

Either way, the team that scopes the work is the team that does the work. No bait-and-switch.

We ship the way we wish our clients shipped.

• Eval data and prompt history versioned in the repo, not lost in Slack
• Agent configurations and tool integrations documented as code, not tribal knowledge
• Human-in-the-loop hooks built in from day one, not bolted on at month six
• Cost and latency budgets tracked alongside functional requirements
• A bias toward shipping the simplest thing that works, then adding only the next thing that's earned

Built systems where "good enough" wasn't.

25 years of engineering leadership across Fortune 500 companies, from individual contributor through VP. Highly regulated, high-stakes industries with constraints that don't tolerate hand-waving. The judgment that comes from shipping real systems under real consequences, applied now to AI-bearing software.

Where we've shipped.

• Telecommunications
• Aerospace
• Avionics
• Medical Devices
• In-Flight Entertainment