Answers from your documents
Search approved files and prepare answers with the relevant source context, rather than relying only on a model’s general knowledge.
AI agent development
AI agents that turn information into useful work.
Give your team a researched brief, a document-backed answer or a useful first draft. We build AI agents around your information, tools and approval rules.
Book a discovery callFor agencies, advisory teams and businesses with knowledge spread across files and systems.
Connected by design- Your approved sources
Documents, business data and relevant research
- An agent with a defined task
Retrieve context, use tools, prepare the output
- Your team’s review
Check the answer before an important action
Start with a task your team repeats.
An agent is most useful when its job is specific. We map the inputs, the tools it may use and what a good answer looks like before choosing a model or building the workflow.
Research and structured briefs
Gather company or topic information, extract the useful details and prepare a consistent brief for a person to assess.
Drafts connected to your process
Prepare summaries, reports or next-step drafts, then route them into the tools where your team works.
Related engineering work
Experience building conversational analytics.
As Founding AI Engineer at Analytics Depot, Waleed Abbas built conversational analysis, document retrieval and a multi-provider AI backend, including the production safeguards around those features.
Explore Analytics Depot
Analytics DepotB2B analytics SaaSQuestions before we start.
Practical answers for the team that will use the system.
What is the difference between an AI agent and a chatbot?
A chatbot mainly handles a conversation. An agent can also retrieve information, use approved tools and carry out defined steps. The useful distinction is what it can actually do in your workflow, and which actions still require a person’s approval.
Can an agent use our business documents?
Yes, where access and file formats allow it. We can build retrieval over approved documents so the agent has relevant context. We agree on access, updates and source references, then test whether the answers are useful for your team.
Can the agent take actions automatically?
Selected actions can run automatically within an agreed scope. Sensitive or consequential steps can require approval. We define the allowed tools, permissions and fallback behavior before connecting the agent to a live business process.
How do you evaluate an AI agent?
We use examples from the intended workflow, including ambiguous questions, incomplete documents and failed tool calls. We assess output quality and whether the agent respects its boundaries. The acceptance checks are agreed around the task, rather than a generic accuracy promise.
Bring one real workflow.
We’ll map the useful starting point, the tools involved and the checks it needs.