AI integration services that ship to production

From a single automation to agents across your stack

Most companies don't need a new AI product - they need AI woven into the systems they already have. Here is what the work covers.

Workflow & process automation

Automate the manual, repetitive work - data entry, routing, document handling, reporting - so your team stops doing it by hand.

LLM & RAG integration

Connect large language models to your own data with retrieval, so answers are grounded in your business instead of the open web.

AI agents

Agents that take real actions across your stack - read context, decide, and execute inside the systems you already use.

Custom copilots & chatbots

Support, sales, and internal copilots tuned to your domain, your tone, and your guardrails - in production, not a demo.

Data pipelines & enrichment

The ingestion, cleaning, embedding, and pipelines that feed AI features reliable, current data.

Model selection & evaluation

Pick the right model for each job - Claude, OpenAI, open weights - and measure quality, cost, and latency instead of guessing.

Common places AI earns its keep first

• Customer support - deflect repetitive tickets, draft replies, summarize threads
• Sales & CRM - qualify leads, enrich records, draft outreach, route conversations
• Operations - automate back-office workflows, document processing, and approvals
• Internal knowledge - a copilot over your docs, wikis, and tickets
• Content & marketing - generation, repurposing, and publishing pipelines
• Engineering - AI in your own product features and developer workflows

Prototype first, then build what works

We build production AI - our own, every day

We don't integrate AI from a slide deck. These are AI products we built and run ourselves - the same engineering we bring to your integration.

CoPillyAI co-pilot for LinkedIn - human-like comments and replies.AriSendAI sales rep across Facebook, Instagram, WhatsApp, and Telegram.ContentForgeAI content pipeline - aggregate, process, auto-publish.OpsBirdAI incident response that finds Kubernetes root causes.CleanyAIAI text cleaner - publication-ready output in one paste.

Common questions

What are AI integration services?

AI integration services bring artificial intelligence - large language models, agents, and automation - into the software and workflows a business already uses, rather than building a brand-new AI product from scratch. In practice that means connecting models to your data, automating manual processes, and shipping copilots or agents into your existing stack with the monitoring and guardrails to run them in production.

How much do AI integration services cost?

It depends on scope. A focused automation or a single LLM feature integrated into an existing system is a small, fixed engagement; a broader rollout across multiple workflows with custom agents and data pipelines costs more. We start with a short prototype against your real data so you can see the value before committing to the full build, then scope a fixed, itemized quote.

How long does AI integration take?

A first working prototype against your data usually takes a couple of weeks. A production integration - wired into your stack, with evaluation and monitoring in place - typically runs from a few weeks to a couple of months depending on how many workflows and systems it touches.

Which AI models and tools do you work with?

We are model-agnostic and pick the right one per job - Claude, OpenAI, Gemini, and open-weight models like Qwen, DeepSeek, and Mistral - and measure quality, cost, and latency rather than defaulting to one vendor. On the engineering side we work across your existing stack and the common AI tooling (vector databases, orchestration, evals).

Do you work with our existing systems and codebase?

Yes. Most AI integration work is exactly that - dropping AI into systems you already run. We integrate with your codebase, databases, and third-party tools, and can join your team for the engagement or deliver it end to end.

Is our data kept secure and private?

Yes. We design integrations so your data stays within boundaries you control - using retrieval over your own stores, choosing models and deployment options that match your privacy requirements, and avoiding training on your data. The specifics are scoped to your compliance needs up front.