AI, Data & Intelligence
Generative AI & LLM Engineering
We build intelligence into enterprise workflows with measured outcomes, governed data access, evaluation and human oversight.
Overview
Why organisations engage us for generative ai & llm engineering
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 generative ai & llm engineering?
Yes. Acmez Technologies provides generative ai & llm engineering services for enterprises, SMEs, startups and regulated organisations. The service includes Generative AI Strategy, Generative AI Applications, Large Language Model Applications, Retrieval-Augmented Generation (RAG), Enterprise Knowledge Assistants, Custom AI Assistants, AI Copilots, LLM-Powered Agents, Prompt Engineering, LLM Integration, Model Fine-Tuning, Vector Database Solutions, Semantic Search, Enterprise Knowledge Systems, LLMOps, 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 Generative AI & LLM Engineering covers
Each capability below is delivered as part of a wider engagement or on its own, depending on what you need.
Generative AI Strategy
Generative AI Strategy within our generative ai & llm engineering services, scoped after a short discovery conversation.
Generative AI Applications
Applications that produce new content for your business, such as proposals, product descriptions, marketing variants, reports and images, with brand rules, approval workflows and provenance built in.
Large Language Model Applications
LLM-powered processing behind the scenes: classifying, extracting, summarising and routing large volumes of text such as emails, tickets, contracts and feedback, often with no chat interface at all.
Retrieval-Augmented Generation (RAG)
Engineering of retrieval-augmented generation pipelines that ground language model answers in your documents, covering ingestion, chunking, hybrid retrieval, reranking, citations and systematic evaluation.
Enterprise Knowledge Assistants
An internal assistant that answers employees' questions about policies, procedures, products and past work in Microsoft Teams, Slack or the intranet, with cited sources and respect for document permissions.
Custom AI Assistants
Purpose-built AI assistants for a specific role or team, such as underwriters, relationship managers or field engineers, that combine your data, tools and procedures in ways off-the-shelf assistants cannot.
AI Copilots
AI copilots embedded inside your own software product or internal platform, helping your users complete tasks in context, with features designed, priced and governed as part of the product.
LLM-Powered Agents
LLM-Powered Agents within our generative ai & llm engineering services, scoped after a short discovery conversation.
Prompt Engineering
Systematic design, testing and versioning of the instructions that drive language model behaviour, so outputs are accurate, consistently formatted and stable when models or inputs change.
LLM Integration
Adding large language model capabilities to existing applications through provider APIs, handling streaming, structured output, rate limits, retries, data redaction and switching between providers.
Model Fine-Tuning
Adapting language models to your tasks, terminology and output formats with supervised fine-tuning and parameter-efficient methods such as LoRA, when prompting and retrieval are not enough.
Vector Database Solutions
Selection, design and operation of vector databases for AI search and retrieval, from pgvector inside PostgreSQL to dedicated engines such as Qdrant, Weaviate, Milvus or Pinecone, tuned for recall, speed and cost.
Semantic Search
Search that understands meaning rather than exact keywords, for websites, product catalogues, intranets and support portals, combining vector and keyword retrieval with relevance tuning and analytics.
Enterprise Knowledge Systems
The content, structure and governance foundations that make organisational knowledge findable and AI-ready: ownership, taxonomies, metadata, knowledge graphs and lifecycle rules across repositories.
LLMOps
The operating discipline for language model applications in production: prompt and model versioning, evaluation pipelines, tracing, guardrails, cost monitoring and safe release of changes.
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 generative ai & llm engineering 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 Generative AI & LLM Engineering
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Where this applies
Healthcare & Life Sciences
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Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
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E-Commerce
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Questions & answers
Questions about Generative AI & LLM Engineering
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 generative ai & llm engineering 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.