What we do
Datasmarts finds the process that is costing you the most hours, builds the system that removes it, and measures what changed. That work falls into four services, and most engagements use more than one.
The starting point is a conversation rather than a specification. A process gets picked because it is expensive and repetitive, not because it is the most interesting thing to automate, and the cost is estimated before anything is built.
Some engagements end at the assessment, because the honest finding is that a process runs too rarely to justify the work. That is a result worth paying for and it is cheaper than discovering it halfway through a build.
AI agents and LLM systems
Your team reads, sorts, and answers the same documents and questions every day, and the volume grows faster than the headcount you can add.
A system that reads the documents, answers the questions, and routes what needs a person, with every answer traceable to the source it came from.
- A retrieval layer over your documents, so every answer cites the source it came from
- Extraction pipelines that turn filings, contracts, and forms into structured records you can query
- Assistants that answer staff or customer questions in Slack, WhatsApp, or whichever channel your team already has open
- An evaluation harness that measures accuracy against an acceptance bar agreed before the build starts
- Model routing, so each step runs on the model that fits it rather than the most expensive one
- A human review queue for the cases the system should not decide on its own
Workflow automation
A process your business depends on runs on people copying data between tools, and it breaks quietly whenever the person who knows it is away.
The process runs on a schedule, and a person reviews the exceptions instead of assembling the whole thing by hand every week.
- The process mapped as it actually runs today, including the steps nobody documented
- Orchestrated workflows that move and validate data between the tools you already use
- Python services for the parts that are really software, where they can be tested
- Validation and error handling, so a bad record stops and alerts instead of spreading
- Reporting that arrives on its own, in the format the people reading it already expect
- Handover documentation, so the workflow is yours to change rather than ours to maintain
AI strategy
You have a list of things AI could do for the business and no reliable way to tell which one pays for itself first, or whether any of them do.
A ranked shortlist that says what each process costs you today, what automating it would take, and what to do first.
- A process inventory: what runs manually today, how often, and how many hours it consumes
- A cost estimate per process, built from your numbers rather than industry averages
- A ranked shortlist with the payback case for each candidate, and the ones not worth doing
- Build versus buy analysis for each candidate, including staying with what you have
- A sequenced roadmap, so the first project is the one that funds the next
- The risks written down: what could fail, what it would cost, and what would have to be true
Data and backend engineering
The automation you want sits on top of data that is slow, scattered across systems, or does not agree with itself from one report to the next.
A data layer that is fast and consistent enough that the systems built on top of it can be trusted without a manual check.
- Ingestion pipelines that pull from your sources on a schedule and fail loudly when a source changes
- A query layer tuned against the access patterns you actually have, not the ones the schema assumed
- Schema and data model work, so the same question returns the same answer in every system
- Backend services and APIs that the rest of your tooling can build on
- Monitoring on the pipeline itself, so a silent gap in the data is visible before someone reports it
- Documentation of where each number comes from, which is what makes reporting defensible
How an engagement runs
The same four steps apply whichever service you start with. Each service page has its own detail underneath them.
Diagnose
Design
Build
Measure
ROI calculator
What would automating this task save you?
Enter what the task costs you today. The estimate updates as you change the inputs, and every assumption behind it is listed below.
Prefer to talk through your own numbers? Get in touch
hours per week
Estimate based on conservative assumptions: 48 working weeks, your selected automation share, fully loaded labor cost. Figures in USD.
Not sure which of these you need?
That is the usual starting point, and it is what the assessment is for. Tell us which process is costing you the most time and we will tell you whether it is worth automating.