AI, Data & Intelligence
Artificial Intelligence
We build intelligence into enterprise workflows with measured outcomes, governed data access, evaluation and human oversight.
Overview
Why organisations engage us for artificial intelligence
We build intelligence into enterprise workflows with measured outcomes, governed data access, evaluation and human oversight.
The work is shaped around your current systems, business priorities, internal capability, data sensitivity and risk tolerance, so the result is a practical engagement rather than a generic service package.
During discovery we document the baseline, dependencies, decision owners and acceptance criteria. That gives search, procurement and leadership teams a clear answer to what is included, why it matters and how the work will be governed.
Does Acmez provide artificial intelligence?
Yes. Acmez Technologies provides artificial intelligence services for enterprises, SMEs, startups and regulated organisations. The service includes AI Strategy & Consulting, Enterprise AI Solutions, Custom AI Development, AI Application Development, AI Solution Architecture, AI Assistants, AI Agents, Agentic AI Systems, Conversational AI, Enterprise AI Integration, AI Model Integration, AI Deployment, AI Proofs of Concept, and can be delivered as a fixed-scope project, dedicated team, staff augmentation, offshore development centre or managed service.
Engagement models
Fixed scope, dedicated teams, offshore development centre, staff augmentation or managed services.
Compare modelsDelivery locations
Roorkee, Uttarakhand and Bengaluru, Karnataka, serving clients in India and internationally.
Contact our teamWhat is included
What Artificial Intelligence covers
Each capability below is delivered as part of a wider engagement or on its own, depending on what you need.
AI Strategy & Consulting
A clear-eyed plan for where AI will create value in your organisation, what data and governance it needs, and which use cases to fund first, without chasing every new model release.
Enterprise AI Solutions
AI applied to recurring enterprise problems such as document-heavy processes, service desks, sales forecasting and knowledge retrieval, rolled out across departments on a shared, governed platform.
Custom AI Development
Bespoke machine learning, computer vision and language models trained on your proprietary data for problems that off-the-shelf AI products and general-purpose models cannot solve well enough.
AI Application Development
Full-stack development of web and mobile applications with AI at their core, covering the interface, backend, model calls, streaming, error handling, access control and the cost of every request.
AI Solution Architecture
Architecture decisions for AI systems: which models to use and where they run, retrieval versus fine-tuning, data flows and permissions, latency and cost budgets, and how the whole system is evaluated.
AI Assistants
Rollout and governance of ready-made workplace AI assistants such as Microsoft 365 Copilot, Gemini for Google Workspace and ChatGPT Enterprise, so licences turn into real productivity rather than idle seats.
AI Agents
AI agents that complete defined tasks by calling your systems, such as updating a CRM record, triaging a ticket or reconciling a payment, with strict permissions, audit logs and human approval where it matters.
Agentic AI Systems
Multi-agent and long-running AI workflows that plan, delegate and coordinate across several systems and teams, with orchestration, shared state, checkpoints and governance designed for production.
Conversational AI
Customer-facing chat and voice assistants on your website, app, WhatsApp and phone lines that resolve common requests, speak your customers' languages and hand over to people with full context.
Enterprise AI Integration
Connecting AI capabilities into the systems your organisation already runs, such as ERP, CRM, ITSM, document management and collaboration tools, with identity, permissions and audit carried through.
AI Model Integration
Wiring trained machine learning models and model APIs into existing software, from real-time scoring endpoints and batch predictions to model versioning, fallbacks and safe upgrades.
AI Deployment
Taking an AI system from a successful pilot to dependable production: hosting, scaling, security review, monitoring, rollback and the operational handover that pilots usually skip.
AI Proofs of Concept
Time-boxed experiments, usually four to six weeks, that test whether an AI idea works on your real data and processes, with success criteria agreed up front and an honest go or no-go recommendation.
What changes
What changes for your organisation
Stated as outcomes we can be held to, without invented figures.
Clearer priorities
The engagement focuses investment on the work that removes the largest operational or growth constraint.
Better delivery control
Scope, responsibilities, acceptance criteria and reporting are made explicit before delivery accelerates.
Systems that can evolve
Architecture, documentation and support practices are designed so future change is manageable.
Lower operational risk
Security, quality, monitoring and continuity expectations are considered from the start.
How we work
How a artificial intelligence engagement runs
A consistent sequence, adapted to the size and risk of the work.
Identify high-value use cases
Candidate use cases scored on business value, data availability, risk and effort, with one or two chosen for a first release.
Prepare data and guardrails
Data access, quality checks, privacy controls and the rules for what the system may and may not do, agreed before any model is built.
Build proof of concept
A working prototype on real, representative data, built in weeks rather than months, to test whether the idea holds.
Evaluate with real cases
Accuracy, failure modes and user acceptance measured against a labelled test set and reviewed with domain experts.
Deploy with monitoring
Production rollout with drift, quality and cost monitoring, human escalation paths and a schedule for retraining or re-evaluation.
Technologies
What we typically build with
Technology is chosen for the problem and for long-term supportability, not from preference. Where your organisation already has a standard, we work to it.
Our engineering standards- Python
- PyTorch
- TensorFlow
- scikit-learn
- LLMs
- RAG
- Vector Databases
- SQL
- Spark
- Power BI
- Tableau
- MLOps
Technology names describe the tools our engineers work with. They do not indicate partnership, certification or endorsement by the respective vendors.
Explore further
Capability that works alongside Artificial Intelligence
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Where this applies
Healthcare & Life Sciences
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Questions & answers
Questions about Artificial Intelligence
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionYes. It can be delivered on its own or combined with related services when the work crosses strategy, design, engineering, data, cloud, security or support.
We begin with a short discovery conversation, review the current state, identify constraints and then provide a written scope with responsibilities, timeline, assumptions and commercial terms.
Yes. We commonly work inside client repositories, cloud accounts, collaboration tools and delivery processes, while documenting decisions so your team can retain control.
Next step
Let us discuss your artificial intelligence requirement
Tell us what you are trying to achieve. We will tell you honestly what it takes, including when a smaller engagement would serve you better.