AI, Data & Intelligence
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.
Capability overview
What large language model applications involves
Many of the most valuable language model applications never talk to a user. They read every inbound email and route it to the right queue, pull key terms from supplier contracts into a register, summarise hundreds of customer reviews into themes, or tag support tickets with product and root cause. These are text processing pipelines where an LLM replaces brittle rules.
Pipelines are engineered for accuracy and cost at volume. Outputs are forced into validated JSON schemas, confidence signals trigger human review, cheaper models handle straightforward items while harder ones escalate to more capable models, and batch processing APIs reduce cost for work that is not time sensitive.
What is included
Common LLM processing tasks
Inbound email triage
Shared mailboxes classified by intent, urgency and customer, with key fields extracted and cases created in your service platform.
Contract data extraction
Parties, dates, renewal terms, liability caps and notice periods extracted into a structured register with page references.
Feedback and review analysis
Survey responses, app reviews and call notes grouped into themes with sentiment, trend lines and representative quotes.
Ticket enrichment
Support tickets tagged with product area, probable cause and duplicate detection to speed up resolution and reporting.
Model routing and batching
Items routed between small and large models by difficulty, and non-urgent work processed through batch APIs at lower cost.
How we work
How we deliver large language model applications
Label a gold set
A few hundred real items labelled by domain experts to define correct outputs and measure accuracy.
Schema and prompt design
Output schemas, instructions and examples written, with edge cases from the gold set covered explicitly.
Accuracy tuning
Prompts, models and routing rules iterated until accuracy and cost per item meet agreed targets.
Pipeline engineering
Queue-based processing, retries, validation and write-back to target systems built for production volumes.
Ongoing sampling
A random sample of production outputs reviewed each week to catch accuracy drift early.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
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.
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Questions & answers
Questions about Large Language Model Applications
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionNot always. With large labelled datasets and stable categories, a traditional classifier can be cheaper and just as accurate. LLMs excel when labels are scarce, categories change often or the text needs reasoning.
Accuracy depends on document quality and task clarity, so we measure it on your own gold set rather than quote general figures. Low-confidence items are routed to people, keeping errors out of downstream systems.
Current large models handle Hindi and several major Indian languages reasonably well, though accuracy varies by language and model. We test on your actual documents before committing.
Costs depend on text length, model choice and volume. We calculate cost per thousand items during tuning so the running cost is known before rollout, and routing plus batching keep it down.
Next step
Discuss large language model applications with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.