Discovery and assessment
A short, fixed-scope piece of work to understand the problem, the data and the options, ending in a written recommendation.
Services
AARHIT Systems offers 38 services organised into five pillars. Each one can stand alone, but most real engagements combine several, for example a feasibility study that leads to a prototype, then to data engineering, integration and MLOps.
Service architecture
Not sure where to begin? Describe the problem and we will suggest a starting point.
How the pillars connect
The right starting point depends on how certain you are about the problem, the data and the solution. This table is a quick guide; our team will refine it with you.
| If your situation is | A sensible starting point | Pillar |
|---|---|---|
| You have an idea but do not know if AI can do it | Technology Feasibility Study or AI Proof of Concept | Research |
| A process is slow, manual and repetitive | Process assessment, then RPA or Business Process Automation | Automation |
| Staff spend hours reading documents or emails | Intelligent Document Processing or NLP | Automation / Hybrid Intelligence |
| You want an assistant or copilot over company knowledge | Generative AI and LLMs with retrieval and evaluation | Hybrid Intelligence |
| A pilot worked but is not ready for real use | AI Application Development, Integration and MLOps | Technology & Engineering |
| Leadership wants an AI plan with priorities | AI Strategy and Technology Consulting | Technology & Engineering |
| Equipment or sites generate data nobody uses | IoT and Edge AI or Digital Twin Development | Advance Technology |
| AI is already in use and risk is unclear | AI Security, Governance and Ethics | Technology & Engineering |
Pillar 01
Assessing emerging technology and engineering the connected products, cloud AI, IoT and digital twin systems that follow from it.
Assess, test and adopt new technologies against a real operating question before committing budget to them.
Explore serviceRedesign processes, systems and data flows so that digital tools change how work is actually done.
Explore serviceDesign and build software and connected products with AI capabilities built into the core of the product.
Explore serviceBuild AI workloads on managed cloud services with sensible choices on cost, data residency and scale.
Explore serviceConnect devices and sensors, and run inference close to where data is produced when latency or connectivity demands it.
Explore serviceCreate data-connected virtual models of assets, processes or facilities for monitoring, simulation and planning.
Explore servicePillar 02
Eight services covering rule-based, process, AI-assisted and agentic automation, with human oversight designed into every level.
Combine workflow automation with machine learning and language models to handle processes that rules alone cannot.
Explore serviceAutomate repetitive, rule-based tasks across applications that lack usable APIs, with proper exception handling.
Explore serviceEmbed language models, classifiers and retrieval into business workflows with validation and review steps.
Explore serviceDesign AI agents that plan and act through approved tools within defined permissions, budgets and approval gates.
Explore serviceDigitise approvals, hand-offs and cross-department processes end to end, with clear ownership and audit trails.
Explore serviceConnect plant systems, sensors and control data with analytics and AI for monitoring, quality and maintenance.
Explore serviceExtract, classify and validate information from documents with confidence scoring and human review queues.
Explore serviceAutomate build, test, deployment and infrastructure provisioning so software reaches production reliably.
Explore servicePillar 03
Research that answers practical questions: will this work, with this data, at this cost, and what should we build next?
Investigate whether and how AI methods can solve a specific organisational problem, with experiments that answer it.
Explore serviceExplore modelling approaches, features and training strategies for problems where the right method is not yet known.
Explore serviceBuild a focused, time-boxed proof of concept that tests one important assumption with real or representative data.
Explore serviceAssess technical, data, operational and cost feasibility before an organisation commits to a build.
Explore serviceMeasure model quality, bias, robustness, latency and cost, then tune models and prompts against clear criteria.
Explore serviceTurn validated research into a usable prototype or minimum viable product that real users can test.
Explore servicePillar 04
AI capabilities for language, vision, prediction and decisions, designed around the people who use them and remain accountable.
Design systems where people and AI share work, with clear roles, explanations, overrides and feedback loops.
Explore serviceConnect data, models and business rules to support specific decisions, while the accountable person keeps the final call.
Explore serviceBuild grounded generative AI applications using large language models, retrieval, evaluation and guardrails.
Explore serviceCreate assistants for customers or staff that answer from approved knowledge and hand over to people when needed.
Explore serviceDetect, classify, measure and inspect using images and video, from cloud pipelines to on-device inference.
Explore serviceClassify, extract, search and summarise text across languages, including Indian language requirements where relevant.
Explore serviceForecast demand, risk, failure or behaviour with models that are validated, monitored and explained to users.
Explore serviceEncode domain rules, knowledge graphs and expert reasoning, often alongside machine learning and language models.
Explore serviceBuild systems that reason across text, images, audio, video, documents and sensor data together.
Explore servicePillar 05
Engineering, data, infrastructure, governance and quality work that moves systems from prototype into maintainable production.
Engineer software around your processes and data where packaged products do not fit the requirement.
Explore serviceBuild web, mobile and internal applications with AI features that users trust and can actually work with.
Explore serviceConnect models and AI services with ERP, CRM, databases and legacy systems through secure, observable interfaces.
Explore serviceBuild pipelines, warehouses, lakehouses and analytics that give AI and reporting reliable, governed data.
Explore serviceVersion, deploy, monitor and retrain models with repeatable pipelines, so they stay accurate after launch.
Explore serviceDesign compute, storage, networking and GPU capacity for training and inference with cost and security controls.
Explore servicePut policies, risk assessments, security controls and accountability around AI systems throughout their life.
Explore serviceIdentify where AI is worth pursuing, prioritise use cases and plan the capability, data and governance to deliver them.
Explore serviceTest software, automations and AI behaviour with automated suites, evaluation sets and quality gates.
Explore serviceEngagement models
We shape the engagement to the level of uncertainty. Early work is short and bounded; engineering work is planned in releases.
A short, fixed-scope piece of work to understand the problem, the data and the options, ending in a written recommendation.
A time-boxed build that tests the riskiest assumption with real or representative data and agreed success criteria.
Design, build, integration and release of a defined system, delivered in increments that are reviewed as they land.
Monitoring, improvement, retraining and support after release, with service reviews and clear escalation routes.
Portfolio available on request. Selected work and project references can be shared during relevant business discussions. Request a conversation.
Next step
Describe what you are trying to achieve. We will suggest a starting point, explain the trade-offs and tell you what information would help us give a better answer.