Teams buried in knowledge work
Support, sales, operations and finance teams that spend hours reading documents, looking up records and writing the same kinds of answers.
Business applications
AI agent and generative AI development
Give repetitive knowledge work to AI agents that use your data, follow your rules and hand decisions to a person when it matters. We build custom agents, RAG assistants and generative AI applications connected to the systems your business already runs on.
Tell us what you need
AI agent development services built around one task
An agent is useful when it owns a defined piece of work: triage incoming requests, prepare a quote from a product catalog, reconcile an invoice against an order, research a lead before a sales call. We start from that task, list the information and actions it needs, and decide where a person must confirm the result.
The agent gets narrow tools rather than broad access: read this table, create a draft in that system, send a message to this channel. Every action is logged. When the task needs several steps or roles, we split it between cooperating agents, each with its own instructions and permissions.
Generative AI development services and LLM applications
Not every generative AI application is an agent. Extracting fields from contracts, summarizing call notes, drafting product descriptions in your style or searching across years of project documents are LLM applications with a clear input and output. As a generative AI development company we build them as part of your existing applications or as small internal tools.
We build agents on OpenAI and Claude models, with Amazon Bedrock and Azure OpenAI when the models must run in your cloud account. Agent workflows are built with LangGraph or CrewAI when the orchestration is complex, and prototypes can start in Dify. The Model Context Protocol gives agents a standard way to reach your tools.
AI integration services for your existing systems
An AI feature is only as useful as the data it can reach. Our AI integration services connect models to your document stores, databases, CRM, ERP and ticketing systems through their APIs, with the same access rules your employees have. When a system has no API, we build a small integration layer and treat it like any other part of the application.
Some organizations already run agent platforms. We build agents on Microsoft Copilot Studio for Microsoft 365 tenants and integrate agents with Agentforce for teams that run their customer operations there, so the assistant works where people already work.
Private AI and RAG development services
Retrieval-augmented generation (RAG) lets a model answer from your documents instead of its general training. We build the pipeline end to end: ingesting documents with their permissions, splitting and indexing them, retrieving the right passages and generating an answer that cites its sources.
When documents cannot leave your environment, the whole pipeline runs inside it. We deploy self-hosted models running with Ollama for smaller workloads and self-hosted inference with vLLM for production traffic, on your servers or in your cloud account. You get the same assistant without sending data to an outside model provider.
AI strategy consulting and AI implementation services
AI strategy consulting with us is short and practical. We interview the teams involved, review the processes and data, and rank the use cases by value, feasibility and risk. The result is a first milestone with a defined scope, test set and running-cost estimate.
AI implementation services continue from there: building the first use case, putting it in front of real users, measuring it and then moving to the next one. Each milestone is priced before it starts.
Agent products we build and run
We apply the same methods to our own products. ZooGent is our open-source framework for building AI agents and assembling them into teams that run on the orchestrator you already use. The GEO Agent Team is a team of seven agents that helps B2B content and SEO agencies get their clients cited in AI answers.
For workflows that connect tools more than they need judgment, see AI automation. For assistants that answer questions from your company knowledge, see AI chatbots.
Who it is for
Built around the people who use it
Leaders with an AI mandate
Owners and managers who need a first AI use case in production, with clear costs, controlled data access and results they can check.
What is included
The parts needed for a working application
Use case and data assessment
Which task to start with, which data the agent needs, what it may do on its own and how you will measure whether it works.
Agent and application build
Prompts, tools, retrieval, memory and the interface people use, from a chat panel to a step inside an existing application.
Integrations and permissions
Connections to your documents, databases and business systems, with each user's access rules applied to what the agent can see and do.
Evaluation and operation
A test set of real questions and tasks, quality checks before each release, usage and cost monitoring, and a plan for model updates.
How we work
Small milestones and visible progress
Choose the first task
Pick one task with enough volume to matter and a clear definition of a correct result. Collect real examples to test against.
Build, evaluate and release
Senior engineers use AI-assisted delivery to target the first working release in 14 days. We measure the agent against the test set, release it to a small group and widen access as the results hold.
Pricing: Fixed price per milestone, no hourly billing. About a third of a traditional team's quote for the same scope. Model usage and hosting are estimated per use case before we build, and billed by the providers directly.
FAQ
Questions people ask before starting
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent also takes actions: it looks up records, fills in forms, updates systems or hands work to another agent, within the permissions you give it. Many projects start with an assistant that answers questions and add actions once the answers are reliable.
Which models do you use?
We build agents on OpenAI and Claude models, on models available through Amazon Bedrock or Azure OpenAI in your cloud account, and on open models self-hosted with Ollama or vLLM. We choose per task based on quality, cost, speed and where the data is allowed to go, and we can switch models later without rebuilding the application.
Can the AI run without sending our data to an outside provider?
Yes. For sensitive data we deploy open models on your own servers or in your cloud account, so documents and prompts stay inside your environment. Where a hosted model is acceptable, we use providers and settings that do not train on your data.
Do you provide ChatGPT integration services?
Most teams asking for ChatGPT integration want an assistant that knows their own data and works inside their tools. We build that with OpenAI models or an alternative through the API, connected to your documents and systems, with your access rules applied.
How do you know the agent gives correct answers?
Before release we build a test set from real questions and tasks and agree what a correct result is. Each version is measured against it. In production, answers cite their sources, people can flag mistakes and low-confidence cases go to a person.
What does AI strategy consulting cover?
A short assessment of where AI can take real work off your team: candidate use cases, the data each one needs, expected running costs, risks and the order to build them in. It ends with a scoped first milestone rather than a slide deck.
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