AI agents, assistants, and automation, built on data you can trust.
We build AI for specific business tasks: agents that take defined actions in your systems, assistants and chatbots that answer from your internal knowledge, models that classify documents, and workflows that remove manual steps. Some projects start from your CRM and documents. Others need data from the web, which we collect ourselves. Every project starts by confirming the data can support what you want it to do.
AI & Automation services
Four categories of work we deliver to B2B teams. Each engagement is custom-built around your data sources, business question, and delivery stack.
AI Automation
Remove the manual steps from your operations. We build automation that reads, classifies, routes, and updates information across your systems, so your team spends its time on decisions instead of data entry.
Custom AI Agents
Agents scoped to one business task: they retrieve information, reason over it, and take defined actions in your systems. Each agent runs with explicit permissions, logs every action, and pauses for human approval on the steps you decide should not run alone.
LLM-Powered Solutions
Put large language models to work on your unstructured data. Assistants and chatbots that answer from your documentation, classification pipelines, summarization, and retrieval workflows (RAG), grounded in your business data and, when the project needs it, in web data we collect.
Our approach
We do not resell off-the-shelf software. We design each solution around your data, your systems, and the decision it needs to support, and we stay responsible for it after launch.
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Data Readiness
We assess whether your existing data can support the use case, identify what is missing, and prepare the data the application actually needs. When external data is part of the answer, we collect it.
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Right-Sized Modeling
We choose the simplest approach that meets the target, whether that is a set of rules, a pre-trained model, or a custom model trained on your data. Complexity is a cost, not a feature.
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End-to-End Development
We manage the entire AI lifecycle, from defining the problem and building the solution to deployment, monitoring, and optimization.
We start with the data layer
Before we scope a model or a workflow, we establish what the data can actually support: what is in it, what is missing, and how it changes over time. That happens whether the data comes from your systems or from collection we run.
We then name what we monitor, what depends on your systems, and how each kind of problem gets investigated. When we run the collection too, that investigation covers the pipeline and the workflow together.
Recent AI work, described on a call
A retrieval assistant grounded in client documents and collected web data, with evaluation gates before every release, for a client in a regulated industry.
An LLM classification pipeline processing thousands of records per day for a market intelligence team, with sampled human review on edge cases.
We share the evaluation results and the operating cost on a discovery call.
Frequently asked questions
Eight questions buyers usually ask before a first AI engagement.
What AI services does Mint Data offer?
We build AI automation, custom agents, assistants and chatbots, predictive models, and LLM applications such as classification and retrieval. Each project is scoped to a specific business task, built on your data or on web data we collect, and delivered with evaluation results, documented limitations, and monitoring.
How does Mint Data's AI work integrate with our existing systems?
We deliver AI outputs the way your team consumes data: webhook, S3, BigQuery, Snowflake, Postgres, Google Sheets, CRM, or custom API. Models, predictions, and AI-generated content land where you need them, in the format your stack expects.
Where does our data go, and do you train models on it?
Before we build, we document every model and provider that will process your data, and we use provider terms that exclude your data from training. We never use your data to train models for anyone else. If your policy requires that no data leaves your cloud, we can scope the solution to run inside your environment.
Do you build custom AI models or use off-the-shelf solutions?
Both. When a pre-trained model fits the problem, such as classification, entity extraction, or summarization, we use it. Most domain-specific behavior we solve first with retrieval and evaluation, because it is cheaper to operate and easier to correct. We fine-tune or train models on your data when the use case requires it. We recommend the most pragmatic approach, not the most complex one.
Do we need to hire web scraping to work with you on AI?
No. The two services are independent. You can hire AI work on data you already have, with no web collection involved. When a project does need external data, we collect it ourselves, so one team owns both the pipeline and the model, and there is no handoff between a data vendor and an AI team.
We ran an AI pilot that never reached production. What would be different?
Most pilots fail for one of three reasons: the data could not support the task, nobody defined what a good result looked like, or nobody owned the system after the demo. We confirm the data first, we agree on evaluation criteria in writing, and under a managed service agreement we stay responsible for monitoring and re-evaluation after launch. If the data cannot support the use case, we tell you in the first weeks, not at the end.
How quickly can we move from idea to a working AI prototype?
Most AI engagements move from first conversation to a working prototype or proof of concept in 3–6 weeks, depending on data availability, model complexity, and integration requirements. Full production deployments typically require 6–12 weeks. We share a clear timeline before any work begins.
How we engage
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Fixed-price proof of concept
A working sample or prototype in 2 to 6 weeks, at a price agreed before we start. You evaluate real output before committing to more.
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Managed service
A recurring pipeline or AI workflow, monitored and maintained by our team under a managed service agreement that defines what is included.
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Milestone-based project
For one-time builds or handovers to your team. Scope, price, and acceptance criteria are defined per milestone.
Tell us what you want to automate, answer, or predict
Thirty minutes. We will go through your data, your goal, and what would have to be true for an AI solution to work on it.
Book a discovery callFor current Mint Data clients
Book a discovery callIf we already run a data pipeline for your team, we can evaluate where AI can extend it: classification, enrichment, assistants, agents, or automated workflows on top of the data you already receive. We know the sources, the schema, and how the pipeline behaves when something upstream changes, and we bring that into the assessment.
A dataset built for price monitoring is not automatically ready for a model, so we still scope the new use case with you: what you want it to do, what a good result looks like, and what has to be tested before we commit to it.