Building a compliant bot took 512 hours. A unified framework cuts that to 64, launching agents with just 50 lines of code.
A subsidiary of a global aerospace leader found its internal teams were independently piloting AI initiatives without central coordination.
This fragmentation caused redundant work and compliance bottlenecks, with the manual creation of a single chatbot taking approximately 512 hours....
“Everyone was building their own bots. Some teams were ahead, some just starting. We wanted to create a shared foundation that made it easier to build and learn together.”
Navigation, flight planning, and crew management software for the aviation industry.
Data framework and agentic OCR platform for building LLM-powered applications.
Related implementations across industries and use cases
Critical data was scattered across disconnected lakes. Now, 10,000 engineers query a unified platform using natural language.
A bug sat for years because the fix meant a month of digging. AI traced the fragmented code to draft a solution in three days.
Code fixes piled up in backlogs for months. Now, an autonomous Claude agent generates, tests, and submits validated pull requests.
Critical data was scattered across disconnected lakes. Now, 10,000 engineers query a unified platform using natural language.
Serial testing bottlenecked development. Now, parallelized checks validate hundreds of complex conversation paths in seconds.
Aggregating global data caused system lag. Now, AI predicts repair inflows, helping managers match technician capacity to demand.
Standard cloud setups were too rigid for orbital hardware. Now, custom GPU attachments power autonomous AI in space.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
Building a compliant bot took 512 hours. A unified framework cuts that to 64, launching agents with just 50 lines of code.
A subsidiary of a global aerospace leader found its internal teams were independently piloting AI initiatives without central coordination.
This fragmentation caused redundant work and compliance bottlenecks, with the manual creation of a single chatbot taking approximately 512 hours....
“Everyone was building their own bots. Some teams were ahead, some just starting. We wanted to create a shared foundation that made it easier to build and learn together.”
Navigation, flight planning, and crew management software for the aviation industry.
Data framework and agentic OCR platform for building LLM-powered applications.
Related implementations across industries and use cases
Critical data was scattered across disconnected lakes. Now, 10,000 engineers query a unified platform using natural language.
A bug sat for years because the fix meant a month of digging. AI traced the fragmented code to draft a solution in three days.
Code fixes piled up in backlogs for months. Now, an autonomous Claude agent generates, tests, and submits validated pull requests.
Critical data was scattered across disconnected lakes. Now, 10,000 engineers query a unified platform using natural language.
Serial testing bottlenecked development. Now, parallelized checks validate hundreds of complex conversation paths in seconds.
Aggregating global data caused system lag. Now, AI predicts repair inflows, helping managers match technician capacity to demand.
Standard cloud setups were too rigid for orbital hardware. Now, custom GPU attachments power autonomous AI in space.
Analyzing 10TB of weekly telemetry took IT specialists days. Now, engineers ask AI in natural language to instantly retrieve charts.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.