Knowledge & workforce
Software Engineering Copilot
Support code understanding, modernization, testing, documentation, incident analysis, and delivery using repository context.Code modelsAgentic toolsEvaluation
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AI for technology & digital platforms
AI in Technology & Digital Platforms
Software businesses face pressure to embed AI into products while also accelerating their own engineering. We create AI-native products, engineering copilots, intelligent search, support automation, and the model infrastructure to run them reliably at scale.
The opportunity
AI in technology: where it creates advantage.
Every software company now faces the same two-front demand: ship AI capability inside the product, and use AI to move faster internally. The failure mode is identical on both fronts—demos that never reach production because evaluation, grounding, cost control, and observability were afterthoughts.
We build AI-native product capability the way platform engineering should be done: retrieval grounded in the customer’s data with tenancy and permissions preserved, evaluation suites that gate releases, cost and latency budgets per feature, and fallbacks that degrade gracefully instead of failing loudly. The same discipline powers internal acceleration—repository-grounded engineering copilots, knowledge assistants over documentation, and support automation that resolves rather than deflects.
For technology businesses, AI quality is product quality. Users forgive a missing feature; they do not forgive a confident wrong answer. Our work treats reliability, grounding, and honest failure behavior as core product requirements—because that is what separates AI features that retain users from ones that quietly get turned off.
The operating challenge
What makes Technology hard—and where AI pays off.
01
AI feature pressure
Boards and customers expect AI capabilities, but prototypes stall without evaluation, grounding, and production architecture.02
Engineering velocity
Legacy codebases, review backlogs, and documentation debt slow delivery more than model access does.03
Support economics
Ticket volume scales with growth while deflection stays flat and inconsistent.04
Model operations
Prompts, versions, costs, and quality drift across teams with no shared evaluation or observability.Representative outcomes
What production deployments deliver.
3xfaster test coverage expansion with repository-grounded copilots
-50%modernization backlog age on legacy codebases
2.4xmore cases surfaced per support hour with AI triage
GAINX applications
AI applications for Technology.
Configurable solution patterns adapted to your systems, data, controls, and users—not generic software.
Knowledge & workforce
Enterprise Knowledge Assistant
Provide permission-aware, source-linked answers across policies, research, technical documentation, and internal systems.RAGSemantic searchAccess control
Learn more Customer experience & growth
Customer Service Agent
Resolve routine enquiries, retrieve account context, complete approved actions, and transfer complex cases with a useful summary.Conversational AIAgentic workflowsCRM integration
Learn more Knowledge & workforce
Research & Synthesis Workspace
Collect, compare, cite, and structure information from documents, data, meetings, and approved external sources.Generative AIDocument intelligenceCitations
Learn more Intended outcomes
The change we define before choosing technology.
01
Shippable AI product capability
02
Faster engineering cycles
03
Higher automated resolution rates
04
Governed, observable model operations
Frequently asked questions
Useful context before we begin.
01How do we add AI to our product without rebuilding it?
Intelligence is added as a layer: retrieval and reasoning services behind your existing APIs, with the model choices and deployment topology isolated behind contracts. Product teams keep their stack; the AI capability evolves independently.
02How is multi-tenant data isolation handled?
Tenancy is enforced at retrieval—every query is scoped to the customer whose data is being asked about, with permissions checked before generation. Isolation is an architectural property, not a prompt instruction.
03What does model operations look like for a product team?
Versioned prompts and models, evaluation suites in CI, per-feature cost and latency dashboards, and staged rollouts with automatic fallback. The same engineering discipline your team applies to code, applied to intelligence.
04Can you help with our internal engineering velocity too?
Yes—the same grounded systems apply internally: copilots that cite your repositories, knowledge assistants over internal docs, and support automation on your ticket data. Internal use is usually the fastest proving ground for product-grade AI.
Start with the business objective