Machine learning models
Forecast demand, classify records, or flag unusual patterns. Start with a baseline and evaluate models on held-out data before integrating predictions into a workflow.
AI & machine learning
Intelligence built around your work.
AI development, from a practical first use case to a working product integration.
Explore how prediction, document processing, or a knowledge assistant could support your team. Begin with the problem, the available data, and a clear way to evaluate the result.
Discuss an AI project
Choose the right starting point.
Artificial intelligence is a broad set of techniques; machine learning finds patterns in data. Generative AI produces content, while predictive models estimate an outcome. The right approach depends on what your users need and how you will test success.
Knowledge assistants
Help teams find information in approved documents. Retrieval, source references, access controls, and human escalation make answers easier to inspect.
Document processing
Extract fields, classify incoming documents, and prepare summaries for review. Define how exceptions and low-confidence results reach a person.
Recommendations
Use relevant behavioral and catalog data to suggest products or content. Evaluate usefulness alongside latency, privacy, and operating cost.
AI workflow integration
Connect an assistant to existing software through narrowly scoped APIs. Set clear permissions, approval steps, audit trails, and recovery behavior.
Model monitoring
Track output quality, response time, failures, and cost after release. Use these observations to decide when data, prompts, retrieval, or models need updating.
Evidence before expansion.
A convincing demo is a starting point. Production decisions need representative test cases, agreed thresholds, and visibility into failures.
Explore data engineeringStep 1
Define the use case
Choose one workflow, identify its users, and agree on a measurable baseline. Document data access, constraints, and who reviews the output.
Step 2
Test a small prototype
Compare an initial approach against representative examples, including failure cases. Review accuracy, usefulness, cost, and feasibility together.
Step 3
Build the integration
Connect the validated approach to the product, add appropriate access controls, and test both normal operation and fallback behavior.
Step 4
Release and evaluate
Roll out to a defined audience, monitor real usage, and improve against agreed acceptance criteria. Keep a route for human review and rollback.
Explore our work.
Get a closer look at the brands in our portfolio, then talk to us about the scope of your project.
Questions before you begin.
What do AI and machine learning development services include?
A project can include use-case discovery, data preparation, a prototype, model or retrieval evaluation, application integration, and ongoing monitoring. The scope depends on the workflow, available data, and acceptance criteria.
Do we need to train a custom model?
Not always. An existing model, retrieval from approved documents, or a conventional rules-based approach may be sufficient. Compare options against your accuracy, privacy, latency, and cost requirements before committing to training.
Can AI integrate with our existing software?
Integration depends on the APIs, authentication, data formats, and permissions available in your systems. Discovery should establish these constraints and identify which actions need human approval.
Can you guarantee that AI responses are accurate?
No. AI systems can return incorrect or incomplete output. Evaluation, source references, scoped use cases, monitoring, and human review can help manage that risk, but do not eliminate it.
How are security and compliance handled?
Start by identifying the data involved, who may access it, where it may be processed, and how long it may be retained. Controls and contractual requirements must be reviewed for the specific deployment; using AI does not itself establish compliance.
How long does an AI project take?
There is no reliable fixed timeline without a defined scope. Data readiness, integration complexity, evaluation needs, and review requirements determine the plan. A focused prototype can help establish the next stage before a larger commitment.
Further reading on evaluation and risk management:
NIST AI Risk Management Framework
Start with one useful question.
Tell us which workflow you want to improve, what data you have, and what a useful outcome would look like.
Talk to 10Native