/ AI integration services

AI workflows your team can trust in daily work.

We integrate AI into real operations with scoped tasks, retrieval, structured outputs, review paths, evals, logging, and fallback behavior. The goal is not a flashy demo. It is a workflow your team can use after launch.

Plan an AI workflow See relevant work

/ What we build

Narrow automation where reliability matters.

The best AI integration usually starts with one repeated decision or handoff: summarize an account, classify an inbound request, route an exception, search internal knowledge, or draft the next action for review.

Retrieval over company docs, tickets, policies, and customer context.
Structured model outputs that your application can validate.
Human review for financial, customer-facing, or high-risk actions.
Logs, eval sets, and correction loops so quality can improve over time.

/ How it holds up

AI wrapped in software, not bolted beside it.

We treat the model as one part of the system. Permissions, state, data contracts, observability, retries, and user experience all matter if the workflow is going to survive real use.

Clear boundaries for what the model can and cannot do.
Versioned prompts and captured failure cases.
Operational handover so the team understands how the workflow behaves.

/ proof

Relevant work
behind the claim.

/ AI product engineering

TextCortex

Product engineering on AI knowledge workflows, chat interfaces, and user-facing AI systems used by large audiences.

/ Event operations

Varuna

Technical leadership across a CRM and event operations platform with workback plans, email ingestion, AI task generation, and production architecture.

/ related

Keep exploring.

AI readiness audit

Find the workflows, data, and risks worth addressing first.

Human-in-the-loop AI

Design review paths around model output and risky actions.

RAG vs fine-tuning

Choose the right approach before investing in the wrong system.

/ questions

Can you add AI to an existing tool?

Yes. Many projects start with an existing app, spreadsheet, CRM, or internal process. We map the workflow first, then integrate AI only where it improves the work.

How do you make AI reliable enough for daily operations?

We use scoped tasks, retrieval or structured prompts, validators, human review, evals, logs, and fallbacks instead of letting a model act without guardrails.