You don't need a demo chatbot. You need AI that behaves like a real feature: permissioned, monitored, cost-bounded, and reversible. We integrate AI into your existing UI, APIs, events, and data so teams and customers get value immediately without destabilizing your platform.
Most teams can prototype AI fast. The hard part is integrating it into live workflows with real permissions, uptime expectations, and cost constraints. We build AI to operate safely inside your system like any other production capability.
AI only matters if it changes what you can measure. Here's what you can achieve with meaningful AI integration to your current software:
This is a fit when you want AI in your product but you're not willing to gamble reliability.
You want to integrate AI into an existing product without rewriting core systems
You need real permissions, audit logs, and safe fallbacks, not "AI in production with crossed fingers"
Your team is stuck between "AI pressure" from leadership and real engineering constraints
You already have data and workflows, AI just needs the right integration layer
You've seen AI demos that look impressive but have no path to production
You want a "chatbot page" that isn't connected to real workflows
You need a full rebuild (this service is about integration, not replacement)
Each of these ships as a scoped module, API-first, measurable, and reversible. We don't treat AI features as one-off demos—we integrate them like long-lived capabilities that stay stable as your product evolves.
Guided, context-aware help where your team already works.
We embed an assistant directly into your product screens:
Permission-safe answers with source attribution.
We make your product searchable across internal data and documents:
Triage and routing without manual sorting and handoffs.
We automate routing so work moves on its own:
Structured data from messy documents, with a human review lane.
We turn PDFs and emails into data your system can use:
Next-best-action from real usage, not guesswork.
We add personalized suggestions based on behavior and context:
Explain, detect, and surface insights inside your product.
We surface insights so teams see what's happening without digging:
Every engagement produces artifacts your team can immediately use
that fits your existing architecture
shipped with feature flags + rollback paths
no flying blind
aligned with OWASP LLM security risks (prompt injection, insecure output handling, excessive agency)
to expand from one feature to a repeatable AI capability
so you can maintain and iterate without us
This isn't "add an AI feature." We integrate AI into production workflows with the same discipline as any critical system: safe, observable, secure, and maintainable.
Feature flags, clear success metrics, rollback-ready releases.
Fallbacks for low-confidence outputs and dependency failures.
Least-privilege access + output validation and policy controls.
Quality, latency, errors, and cost tracked with dashboards/alerts.
Clean interfaces, versioning, and modular components you can extend.
Budgets per action, throttles, caching, and model routing.
We pick tooling based on your constraints (security, latency, cost), not trends.
LLMs: OpenAI, Claude, Gemini, and open-source when needed
RAG: Structured retrieval + tool calling (LangChain/LlamaIndex-style)
Vector/Search: Pinecone or Postgres pgvector (when simpler is better)
LLMOps: evaluations, drift monitoring, regression checks
Cloud: AWS / Azure / GCP based on security + data residency
This is not a "throw AI at the problem" approach. It's systematic production integration. A repeatable process that keeps scope tight and shipping real.
01.
Choose the smallest AI capability tied to a measurable KPI (time saved, ticket reduction, conversion lift).
02.
Define where AI sits in your architecture: UI entry points, APIs/events, permissions, data flows, and guardrails.
03.
Ship the module with fallbacks, instrumentation, and cost caps. Test with real users in controlled rollout.
04.
Add evals, monitoring, alerting, edge-case handling, and operational runbooks.
05.
Use real usage signals to improve quality and prioritize the next module(s).
We integrate AI into your existing system, not around it—permissions, data boundaries, and workflows stay consistent with how your product already operates.
We ship with operational safety from day one—feature flags, rollback paths, monitoring, and failure modes are part of the first release, not an afterthought.
We design for governance and auditability—access control, logging, and traceable outputs so teams can explain what happened and why.
We control cost as a product constraint—budgets per action, throttles, caching, and usage reporting so spend doesn't surprise you.
We leave your team stronger—clean architecture, docs, and runbooks so you can extend and maintain the AI layer without being locked to us.
Answers to the most common pre-engagement questions.
We scope access with least-privilege permissions, validate outputs before actions, and design around known LLM security risks like prompt injection and insecure output handling. Every integration includes security review and documentation of data flows.