AI integration services that ship to production
We bring AI into the business you already run - automating workflows, wiring large language models and agents into your stack, and grounding them in your own data. The same engineering discipline behind our own AI products, pointed at your operations.
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 integration
- Agents that take real actions across your stack - read context, decide, and execute inside the systems you already use, with guardrails and evals.
- 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
Agents that act - across the tools you run
A chatbot answers. An agent does. AI agents integration is the work of wiring agentic AI - systems that read context, decide, and take real actions - into your existing stack, so they update records, route requests, and run processes inside the tools you already use. The hard part is not the model; it is doing it safely.
Where agents act
Across your CRM, help desk, databases, spreadsheets, and internal apps - reading context from one system and taking action in another through APIs, webhooks, and tool calling.
Guardrails & permissions
Least-privilege access, human-in-the-loop checkpoints on costly actions, and clear boundaries - so an agent operates only where you let it, and never further.
Evals & monitoring
We measure agent quality against real cases, log every action, and watch cost and latency - so you trust what the agent does in production instead of hoping it behaves.
New to the idea? Read what an AI automation agency does, or see our AI automation agency service.
Prototype first, then build what works
Find the leverage
We map where your team spends time and pick the highest-return places for AI - not the flashiest.
Prototype
A working proof against your real data, so you can judge quality before committing to a build.
Integrate
Wire it into your stack with the guardrails, evals, and monitoring that keep it reliable in production.
Ship & measure
Launch, track quality and cost, and iterate - so the system gets better with use, not worse.
What could automation give your team back
Set your team size, the hours lost to repetitive work, and the workflows you'd hand to AI. We'll show the potential hours and time value you could reclaim - a transparent ballpark, not a promise. For a build budget, try the AI app development cost calculator.
Potential reclaimable
133hrs / month
About $71,885 of time value a year, at your inputs.
- Repetitive hours / month
- 277
- Typical reclaim rate
- 48%
A transparent potential, not a promise. The real figure depends on your actual processes, data, and how much stays human-in-the-loop - which is exactly what we scope first.
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.
- What is AI agents integration?
- AI agents integration means connecting agentic AI - systems that read context, decide, and take actions on their own - into the tools and workflows you already run. Instead of a chatbot that only answers, an agent can pull data from your CRM, draft and send a reply, update a record, or kick off a downstream process. The integration work is wiring those agents into your stack safely: scoped permissions, guardrails, human-in-the-loop checkpoints, and evals so you can trust what they do in production.
- Can AI agents work with our existing tools?
- Yes - that is the point. AI agents integration connects agents to the systems you already use through APIs, webhooks, and tool calling, so they act inside your CRM, help desk, database, spreadsheets, and internal apps rather than in a separate silo. We scope each agent to least-privilege access, add approval steps where a mistake would be costly, and monitor every action so the agent stays inside the boundaries you set.
- 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.
Where does AI fit in your business?
Tell us what your team spends time on - we'll map the highest-leverage places to start, no obligation. Smaller team? Read AI integration for small business.