Data Intelligence & Operational AI
A forecast needs to inform a plan. An extracted document needs to reach the right process. An answer needs context the reader can use. We bring data intelligence and AI into the decisions and work your people are responsible for.
Talk to UsA Situation We Hear Often
This is how the problem usually shows up, and what changes at each step of the fix. Switch to By Service to see the work behind it.
Your People Already Use AI. The Business Does Not Yet.
In most companies AI has arrived through individual people, not through the business. Follow where that leaves things, what AI can take on, and how it becomes part of daily work.
AI Has Arrived, One Person at a Time
People use AI tools on their own, without agreed rules
The tools cannot see your own data, so the answers stay general
Documents are still read and re-keyed by hand
A trial looked promising, then did not reach daily work
Forecasts live in one person’s spreadsheet
It is hard to tell whether an AI answer is right
We start with one job where the time goes, with your people reviewing what AI prepares.
What We Can Work On Here
Business Questions & Knowledge Assistance
Ask questions across business records and documents, then examine the measures, comparisons, and source information behind the answer.
Ask Your Business Data
Pick a question an owner might ask. Each answer is a prepared example using sample data, showing how an AI analyst connects the explanation, the chart, and the records behind it.
Your Question
Why did revenue fall when we completed more orders? Separate the effect of order volume, sales mix, and pricing.
Business Data Assistant
Revenue fell 8.9%, from $900k to $820k, while completed orders rose 10%. The extra orders were lower-value work, while higher-value orders declined.
Volume Increased
Orders rose from 1,000 to 1,100. At the previous average value, that extra volume would add $90k in revenue.
The Mix Changed
Higher-value orders fell from 400 to 300; standard orders rose from 600 to 800. At prior prices, that mix shift reduces revenue by $140k against the volume-adjusted baseline.
Realized Pricing Was Lower
Revenue per higher-value order fell from $1,500 to $1,400, a further $30k reduction. Together: +$90k volume, −$140k mix, −$30k pricing = −$80k.
Completed orders, order categories, invoice line values, and realized revenue per order for two equal periods. Higher-value orders: 400 × $1,500, then 300 × $1,400. Standard orders: 600 × $500, then 800 × $500.
The records show where revenue changed, but not why demand or pricing changed. Compare customer groups, availability, and discounts before deciding what to change.
Scope of Work
Each of these can be a project on its own or part of a larger one. We start with the one your situation calls for.
01
AI and machine learning trained on your own history and the context around it estimate demand, stock, staffing, and other likely outcomes that inform a plan.
02
Learned patterns spot the unusual transaction, the change in performance, and the thing that does not fit, among activity too routine and too large to check by hand.
03
Rank what deserves attention first and suggest the next step, with the reasoning shown so a person can check it before acting.
04
Interpret and organize documents, classify information, retrieve relevant knowledge, and prepare answers with source context.
05
Answer questions in plain language using business data, reports, and documents, with agreed measures, source references, and access permissions so people can check and use the answer.
06
Connect interpretation to tasks, records, and permitted actions in existing applications, with approval and exception paths.
07
Plan how data platforms, analytical methods, models, and business applications connect, including access, evaluation, human review, and how the solution will be operated.
08
Assess outputs against the job, handle uncertainty, and give people a way to check, correct, or stop an action.
Case Studies
Real projects in this area: the business problem, what we built, and what changed.
Logistics & Supply Chain
A freight settlement business moved from manual paperwork to automated extraction, evidence checks, and structured results. The supplied estimates put manual review at roughly 10% of submissions, with typical single-page processing under a minute.
Development Workflows
A client needed a better way to move everyday software requests from the business to its development team. We connected request intake, task tracking, AI-assisted code changes, and developer review, moving from a two-week sprint cycle to releases in 1–2 days.
Healthcare
A clinical intelligence layer for EMRs, designed to bring relevant history, recorded conditions, and clinical findings into one review.
How We Work
Choose a decision or task with a clear purpose. Assess the information available, define useful and unacceptable outputs, and connect evaluation and review to the process before expanding its role.
Something with a clear purpose and a person who owns it, such as a forecast that feeds a plan or a document that feeds a process.
We look at the records you hold before promising a result, so you know what they can reliably show.
The outputs that help, and the ones that must never happen, are defined with your team and tested against real examples.
The result lands in the right record, task, or approval, and a person reviews what the consequences call for. Its role grows once it has proved itself.
Measuring It
These questions help define the improvement with your team and the measures appropriate to the engagement.
What It Looked Like in One Project
~10%
Need manual review, down from all submissions
Read the Case StudyFor Technical Leaders
Whether the information you hold is complete and reliable enough for the purpose, established before anything is built.
The right method for the question. Predictive analysis does not always require a generative model.
Which sources the AI may read, how it finds the relevant part, and how access rules carry through.
What a useful output and an unacceptable one look like, agreed in advance and tested against real examples.
What the AI may do on its own, and what waits for a person to review.
How quality is watched once it is in use, and what it costs to run as usage grows.
These are the platforms and tools we use most for this work, and we are not limited to them.
Claude
OpenAI
Gemini
Llama
Mistral
Hugging Face
Amazon Bedrock
Azure OpenAI Service
Google Vertex AI
Vercel eve
LangGraph
LangChain
VapiAmazon Bedrock AgentsAzure AI FoundryGoogle Vertex AI Agent Builder
Amazon Textract
Azure AI Document IntelligenceGoogle Document AI
PostgreSQL pgvectorRDS
Elasticsearch
Snowflake CortexAmazon OpenSearch ServiceAzure AI SearchGoogle Vertex AI Search
Python
scikit-learn
PyTorch
MLflowSnowflake
Databrickson AWSAmazon SageMakerAzure Machine LearningGoogle BigQuery MLIf your business runs on a different stack, we adapt to it and build on what you already have.
Reporting and business analytics explain performance and its causes. Data intelligence extends into prediction and decision assistance, while operational AI brings interpretation into the work itself. Rules-based automation can execute the next step, with governance defining what is permitted.
An existing report, deterministic rule, or standard feature may handle a straightforward need. The method should fit the information and the task.
A Useful Starting Point
What the AI may read is decided first, and it follows the access rules your people already have. Sensitive details can be held back before anything reaches a model, and the choice of model and where it runs takes the sensitivity of the information into account.
The workflow needs a way to identify uncertainty, correct errors, and prevent an unchecked result from triggering an inappropriate action. Review requirements depend on the task and its consequences.
Where the interfaces support it, AI can prepare or initiate actions within agreed permissions, checks, and approvals. The scope depends on the consequences of the action.
Sometimes a report, a clear rule, or a standard feature handles the need, and we will say so. Forecasting and anomaly detection often use established analytical methods, not a generative model. The method should fit the information and the task.
You do, always. The data, the prompts, the evaluation sets, and the code we build belong to your business, and they stay with you if the engagement ends.
Share the problem, its business impact, and any systems or constraints involved. We can work from the business context, the technical detail, or both.
