Runs outside office hours
An agent answers customers, processes incoming data and moves workflows forward at 3 a.m. the same way it does at 3 p.m., without anyone on call.
AI agent development
Custom AI agents inside your existing tools
We build agents on GPT, Claude or Gemini that answer customer questions, qualify leads, query your data and run multi-step workflows. Each one learns from your own documentation, connects to your systems, and hands the conversation to a person when it isn't sure of the answer.
- Timeline agreed up front
- GPT, Claude and Gemini
- Guardrails and human review
Why an agent
The work an agent takes off your team
Agents are good at repeatable work that follows your rules: the question support answers many times a day, the qualifying questions every new lead gets, and the report someone rebuilds every Monday morning.
Handles volume without new hires
One agent can hold hundreds of conversations at the same time, so a launch or a seasonal spike doesn't mean hiring and training temporary staff.
Costs follow usage
You pay for model API calls and hosting, not for idle capacity. We match the model to the task, so a simple request doesn't run on the most expensive model available.
Use cases
The jobs we build agents for
Most projects start with one of these. Once the first agent is live, its integrations and knowledge base make the second one faster to build.
Customer support agent
Answers customer questions from your docs and knowledge base, troubleshoots common issues, and passes the conversation to a person when a case needs judgment.
- Trained on your documentation and FAQs
- Works inside Zendesk, Intercom and Slack
- Replies in the customer's language, from more than 50
- Hands off to your team with the full conversation attached
Sales and lead qualification
Asks the qualifying questions your sales team would ask, answers product questions, books the meeting, and logs the whole exchange in your CRM.
- Scores leads against your own criteria
- Books meetings through Calendly or Cal.com
- Syncs with HubSpot and Salesforce
- Sends follow-ups based on what the lead actually said
Data analysis agent
Lets anyone on your team ask a question about your data in plain language, turns it into SQL, and returns the answer as a table, a chart or a scheduled report.
- Converts plain-language questions to SQL
- Generates recurring reports on a schedule
- Flags a metric when it moves outside its usual range
- Connects to the dashboards you already use
Workflow automation
Takes over repetitive steps such as data entry, email triage and document processing, and chains them into multi-step workflows across your systems.
- API integrations and webhooks
- Reads PDF, CSV and Excel files
- Sorts incoming email and routes it to the right person
- Runs multi-step workflows with checkpoints you define
How it works
From the first call to a live agent
Each project moves through the same stages, and the timeline depends on scope: a single support agent is a smaller build than several agents working together. You receive the timeline with the quote.
Build and train
We map your use case and design the conversation flow, connect the model to your product, load your documents into a retrieval (RAG) knowledge base, and wire up the integrations.
Test and refine
We test the agent against real conversations and edge cases, measure its accuracy on your own questions, and adjust prompts, retrieval and escalation rules until the results hold up.
Deploy and improve
The agent goes to production with monitoring and analytics. We review what it gets wrong, feed the corrections back in, and add new capabilities in short cycles.
Stack
The models and tools we build with
We choose the model and the framework for each project based on accuracy on your tasks, response time and cost per request, and we can switch models later without rebuilding the agent.
- Models
- OpenAI GPT, Anthropic Claude, Google Gemini
- Frameworks
- LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel
- Memory and RAG
- Pinecone, Weaviate, Chroma, vector embeddings, semantic search
- Integrations
- Slack, Discord, WhatsApp, email, Zendesk, Intercom, HubSpot, custom APIs
- Infrastructure
- AWS, GCP, Azure, serverless functions
Capabilities
What our agents do beyond replying to messages
A chatbot replies to messages. An agent also keeps context between sessions, calls your tools to get the work done, and stops to ask a person when it isn't confident.
Context and memory
Agents keep conversation history, preferences and past interactions across sessions, so a returning customer doesn't have to explain the problem again.
Tool use and actions
Agents call APIs, search databases, send emails and create tickets, so a request ends with the work done rather than a link to a form.
Multi-agent systems
Several specialized agents can split a complex workflow between them, with a coordinator routing each task to the agent built for it.
A person in the loop
You set the confidence threshold. Below it, the agent escalates to a person for edge cases and for decisions it shouldn't make on its own.
Knowledge base (RAG)
Agents answer from your documentation, FAQs and internal wiki through retrieval, so when you update a document, the answers change with it.
Multi-channel deployment
The same agent can run in web chat, Slack, Discord, WhatsApp, Teams, email and SMS, with one set of rules behind every channel.
FAQ
Common questions about AI agents
How long does it take to build an AI agent?
The timeline depends on scope, and you receive it with the fixed quote after the first call. We work in short cycles, so you can try the agent while it is still being built.
What does an AI agent cost?
The build is priced on scope, and you get a fixed quote after the first call. Running costs are the model API and hosting, which typically come to $50 to $1,000 a month depending on volume. We choose models and cache responses to keep that number low. Prices in US dollars.
Can an agent work with the tools we already use?
Yes. We connect agents to Zendesk, Intercom, HubSpot, Salesforce, Slack and any system that has an API, and the agent can both read from and write to them.
How accurate are AI agents?
We measure the agent's accuracy on your own questions before launch, and anything below the confidence threshold goes to a person instead of the customer.
What happens when the agent doesn't know the answer?
It says so. Agents are set up to admit uncertainty, fall back to a safe response, and escalate to a person when confidence is low. The corrections your team makes are fed back into the agent.
Do you maintain the agent after launch?
Yes. Maintenance covers monitoring, model and prompt updates, retraining on new material, and new capabilities as your needs change.
Tell us which task you want an agent to take over
In a free call we look at the workflow with you, tell you whether an agent is the right fit for it, and follow up with a timeline and a quote.