Technology services / AI & Automation
AI & automation.
From idea to impact.
For businesses exploring a specific automation or decision-support need. Assess where machine learning, language processing, computer vision or AI integrations fit your data and workflows.
AI & Automation
- Machine Learning Solutions
- Natural Language Processing (NLP)
- Computer Vision
01 / Overview
AI services for a use case you can evaluate
Perivana supports machine learning, language processing, computer vision, predictive analytics and automation. Begin with a specific task, data you are permitted to use and a baseline for the current workflow. A bounded pilot can help assess whether the approach is useful before a wider implementation.
Measure usefulness before expanding the scope
Agree evaluation examples, acceptable errors, human review and operating costs. Compare the pilot with the existing workflow, including the effort needed to check its outputs.
02 / What we deliver
Expertise you can
put to work.
Machine Learning Solutions
Develop and evaluate predictive models against representative data and an agreed task baseline.
Natural Language Processing (NLP)
Explore chatbots, sentiment analysis and language workflows with defined information sources and review needs.
Computer Vision
Evaluate image or video recognition against the intended environment, data permissions and error limits.
Predictive Analytics
Test forecasts against historical examples and the decisions they are intended to support.
AI-Powered Automation
Connect repetitive workflow steps to automation, with approval points, exceptions and fallback behaviour.
Custom AI Solutions
Assess existing models, adaptation or custom development against the data, task and operating constraints.
Plan your project
Start with a use case you can evaluate.
When this service fits
- Repetitive work with a measurable cost or delay
- A need to find patterns in available business data
- Adding AI capabilities to an existing application
Scope to agree
- Use-case feasibility and data-readiness assessment
- A prototype with evaluation criteria and human review
- Integration, monitoring and operating-cost considerations
What to bring
- The task to improve and how success will be measured
- Available data, usage permissions and quality concerns
- Human oversight, system integrations and budget constraints
Deliverables, timing, pricing and ongoing support depend on the agreed scope. Use these points to shape your enquiry.
Explore custom application integrationDefine the finish line
Define useful behaviour before choosing a model.
An AI pilot should answer a business question with evidence. Agree how the team will compare results, handle uncertainty and keep people in control of consequential actions.
A baseline and evaluation set
Describe the existing task and collect representative examples you are permitted to use. Define useful, incorrect and unacceptable outputs, then compare the pilot with the current workflow.
Data and action boundaries
Document permitted data sources, access rules and retention needs. Decide which actions require human approval and how to handle missing information or an unreliable answer.
Operating limits
Agree latency and cost budgets, monitoring and a fallback when the model or upstream service fails. Include version changes and re-evaluation in the maintenance plan.
03 / Tools & technologies
AI Tools & Technologies
- TensorFlow
- PyTorch
- OpenAI
- Scikit-learn
- Keras
- Microsoft Azure AI
- AWS AI/ML
- Google Cloud AI
- NLP Libraries
- Computer Vision APIs
Why Perivana
Connect an AI pilot with the workflow around it
Perivana combines AI work with software engineering, integration, cloud and security capabilities. Our broader BFSI experience includes AI implementation. Scope a pilot around a business task, then use evaluation evidence to decide whether and how it should enter an operating workflow.
ML, NLP and computer vision options assessed for fit
A baseline and evaluation examples agreed upfront
Data permissions and human review made explicit
Integration, fallback and running costs considered
Practical answers
Frequently asked questions.
AI adoption often raises important questions. Here are answers to the most common ones:
What should decide whether an AI pilot moves to production?
Use agreed evaluation examples and review failure cases, not just a polished demonstration. Compare task quality, review effort, latency and operating cost with the baseline. Decide who owns exceptions and whether the benefits justify the remaining risks and maintenance work.
Do we always need to train a new model?
No. Rules, existing models or retrieval from approved information may suit the task. The right approach depends on the data, accuracy requirements, operating constraints and evaluation results. Start with a bounded use case and increase complexity only when the evidence supports it.
How do we know whether AI is appropriate?
Begin with the business task, available data and a measurable baseline. Agree evaluation criteria, review model errors and define where people must remain involved. A prototype can help assess usefulness and running costs before deciding on a wider rollout.
Do you provide industry-specific AI solutions?
Our BFSI experience includes AI implementation. Enquiries in finance, healthcare, retail, manufacturing or other sectors should identify the task, permitted data and review requirements so that feasibility can be assessed.
How long does it take to implement AI in a business?
Timing depends on data readiness, the use case, evaluation needs and integrations. Scope a pilot with a clear decision point before estimating a wider rollout.
Can AI be integrated with our existing IT systems?
Yes, where suitable interfaces and permissions are available. Define what the AI feature can read or change in your CRM, ERP, cloud service or other application, with approval points and fallback behaviour for failures.
How should model performance be reviewed over time?
Agree evaluation examples, monitoring signals and a review owner. Reassess when data, models or business rules change, and decide whether adjustment, retraining or a different approach is needed. The maintenance scope should define this work.
AI implementation experience
Bring AI into the business context.
Our broader banking and BFSI work includes AI implementation and digital transformation. We connect those initiatives with engineering, security, and adoption priorities across the technology lifecycle.
Read about our BFSI experienceYour next step
Bring us a task worth evaluating.
Tell Perivana which workflow you want to improve, what data is available and how you would judge a useful result. Start with a pilot you can assess.