AI integration for the systems you already run.
We connect language models and agents to your product, help desk, CRM, documents and data, so AI does work inside the tools your team already uses. One workflow from $8,000 fixed, live in three weeks, with evaluations before it goes live.
What AI integration services should include.
Most companies do not need a new AI platform. They need AI inside the systems they already have: a support agent in the help desk, document extraction feeding the ERP, search over the knowledge base, a copilot inside their own product. The hard part is rarely the model. It is the data access, the permissions, the evaluation set that proves the output is right, and the monitoring that catches it when quality drifts after launch. Gigabit builds all of that, and keeps a human in the loop where a mistake would be costly or hard to undo.
Two published examples: a $12M Shopify Plus brand's support agent now resolves 64% of tickets on its own (3,149 a month), first response fell from 4.2 hours to 6 minutes and CSAT rose from 3.9 to 4.5, saving $217,200 in year one. A 140-person consulting firm recovered 1,200 hours a quarter after AI was built into proposals, time capture and knowledge search, with a 5.3-month payback.
Where we integrate AI.
Each integration ships with an evaluation set, guardrails and monitoring, so you can see whether it works.
Customer support
Agents in Zendesk, Intercom, Gorgias or your own help desk that answer from your policies and order data, and hand over cleanly.
Inside your product
Copilots, semantic search, summaries and assistants built into your SaaS product, with cost and quality controls.
Documents and data entry
Extraction from invoices, contracts, forms and emails into the ERP, CRM or database, with confidence thresholds and review queues.
Sales and CRM
Lead qualification, research and follow-up agents working inside HubSpot or Salesforce.
Knowledge and reporting
Answers over your documents and data, with citations, for staff who currently ask a colleague or search five tools.
Guardrails and governance
Access controls, PII handling, logging, human approval steps and evaluation suites, so the integration can pass a security review.
From one workflow to a running system.
Pick the workflow
Measure where time goes and choose the workflow where AI pays back first.
Prove it
Build the evaluation set from real cases before the interface, so success is a number.
Integrate
Wire it into your systems with permissions, guardrails and a human path for edge cases.
Operate
Monitor quality and cost, catch drift, and improve it every month.
Published prices, mapped to the job.
Every price here is the same one on the pricing page. Work with no published price gets a fixed price in writing before it starts.
Figures in the notes are what past clients paid, from the published case studies; they show the range, not a rate card. Work with no natural end, such as ongoing development, is priced per engineer per month with no lock-in.
When to hire us for this, and when not to.
- You can name a workflow where people spend hours on repeatable work.
- The data the AI needs lives in systems you can give access to.
- Someone owns the outcome and can judge whether an answer is right.
- You want AI inside your tools rather than another app to log into.
- A built-in AI feature of a tool you already pay for does the job. Switch it on first.
- Nobody can say what a correct answer looks like. Agree that before building.
- You want a demo for a board meeting. We build for production use.
AI integrated into real operations, with the numbers.
A support agent, AI across a consulting firm, and a patient assistant with clinical guardrails.
A confidence-gated AI support agent now resolves 64% of tickets autonomously, cutting support costs 62% — $217,200 saved in year one.
A phased AI transformation recovered 1,200 consultant-hours per quarter and $480K/year for a 140-person consulting firm — 129% Year 1 ROI.
A RAG patient assistant with clinical guardrails cut repetitive front-desk inquiries 62% and time-to-answer 68% in 8 weeks.
These are past engagements at the prices those clients paid. For build price, monthly cost and payback side by side, see what ten client projects cost.
AI integration, answered
How much do AI integration services cost?
One workflow, scoped, built and deployed with up to three tool integrations, is $8,000 fixed over three weeks. Production agents start at $8,000 as a flat fee. The $25,000 AI Transformation Sprint finds where AI pays off first and ends in a deployed pilot. Ongoing work runs on an embedded team at $3,000–$7,500 per engineer per month, and monitoring after launch from $3,000 a month.
What is the difference between AI integration and building an AI product?
AI integration adds models and agents to software and workflows you already run. Building an AI product means the AI is the thing you sell. We do both; integration is usually faster to pay back because the users, data and workflow already exist.
Which models and platforms do you work with?
Models from Anthropic, OpenAI and Google, run through their APIs or through AWS, Azure and Google Cloud where data residency matters, plus open-weight models where a private deployment is required. We choose per workflow on accuracy, cost and where the data may go.
How do you know the AI is giving correct answers?
Every integration ships with an evaluation set built from your real cases, run before launch and after every change. Low-confidence outputs go to a person. After launch, monitoring tracks quality and cost so drift is caught before customers notice.
Is our data safe?
Data stays in your systems where possible, access follows least privilege, and personal data is minimized or redacted before it reaches a model. For regulated data we use providers and regions that sign the agreements you need, such as a BAA for health data.
Often scoped alongside.
From the field notes
Know the workflow AI should take on?
Describe it and the systems it touches. We reply with what we would build first and the fixed price.