Agentic Unconference 2026 - September 19, Chennai
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As an LLM development company, Entrans builds and fine-tunes language model apps that run on your own data. Senior engineers who ship enterprise software take your use case past the demo, with evals, guardrails and cost controls in place from sprint one.
LLM development services cover the design, build and running of software that uses large language models to read, write, summarize or act on text. The work spans use-case scoping, model choice, retrieval, fine-tuning, integration, testing and monitoring in production. In most projects, the model is the smallest part of the job.
✓ Your bottleneck is unstructured text. Contracts, invoices, tickets and emails arrive faster than people can read them. A language model can pull out the fields, sort them and summarize them in seconds. Your team reviews only the exceptions.
✓ The answer exists, but nobody can find it. Policies, SOPs and old project files hold what staff need. Keyword search misses it. Retrieval-augmented generation (RAG) finds the right passage and cites the source, so people can check it.
✓ A request needs judgment, then an action. Support triage, quote prep and data entry follow steps a person can describe but fixed rules can't capture. An LLM with tool calling reads the request, calls your API and hands off to a person when unsure.
✓ The output gets checked before it does harm. Drafts and extractions that a reviewer approves, or that your system of record validates, pay back fastest. Mistakes stop at the review step instead of reaching a customer.
> You're predicting a number from rows and columns. Churn, demand and credit risk scores come from tables, not prose. A gradient-boosted model such as XGBoost is cheaper, faster and easier to explain to an auditor. We'd scope that as classic machine learning through our DataOps and MLOps services.
> You need very small, fast-starting services at high concurrency. For network-heavy infrastructure components where a single binary and a tiny memory footprint matter more than framework depth, Go engineers are the better answer, and we will say so before you sign anything.
> Your source data is scattered or stale. RAG over outdated documents returns confident, wrong answers. Fix the foundation first with advanced data engineering and analytics work. Then add the model on top.
We start with the use case, not the model. Our LLM consulting covers data readiness, build-versus-buy, model shortlists and a cost-per-request forecast, so you know what production will cost before you fund it. This work runs through our generative AI consulting practice.
We build RAG assistants, internal copilots and document workflows that answer from your own data and cite their sources. Retrieval respects your existing permissions, so users only see what they are allowed to see. More on our enterprise AI app development work.
When prompts and retrieval stop moving accuracy, we tune the model. Our LLM fine-tuning services use LoRA and QLoRA adapters on open-weight models, or provider tuning on hosted ones. We score each run against a held-out test set.
We connect models to your ERP, CRM, ticketing and document systems. Tool calling and Model Context Protocol servers let an LLM read and write through your APIs, with each action logged for audit. For multi-step automation, see our agentic AI solutions.
Regulated teams often can't send data to a public endpoint. We deploy models inside your cloud account or data center. That can be a managed service or a self-hosted open-weight model. Either way, your security team can review the network rules, encryption and access controls.
Most pilots stall between demo and rollout, so our LLM services don't stop at launch. We add test suites, prompt-injection guardrails, tracing and cost alerts. We also check each new model version before it ships and tune the system as usage grows.
80%
Reduction in Manual Reconciliation Effort and 2X Faster Payment Processing
We used LLM functions via AWS Bedrock to pull invoice fields from PDFs, scans and structured records, then matched them against purchase orders and goods receipt notes.
70%
Automated 70% of the migration using AI conversion engines and reduced migration transition time by 40%
Our conversion engine used Gemini AI to turn Tableau formulas into DAX measures and write Power Query M scripts for a firm moving onto Power BI and Microsoft Fabric.
175%
Faster flow sheet design and prototyping and 50% increase in commercial project bidding capacity
We built a web-based flow sheet design tool for a water and environment management company, with an LLM layer that pulls past project data matching each unit pattern.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Use RAG when the model needs your current facts, and fine-tune when it needs a fixed format or tone. Retrieval-augmented generation (RAG) keeps knowledge in your documents, so an update means editing a file, not retraining a model. You can also enforce user permissions at query time. Fine-tuning changes how the model responds. That helps with strict output formats, domain vocabulary and lower latency at high volume. Plenty of production systems use both: retrieval for the facts and a small tuned model for the format.
Pick a closed API model such as GPT, Claude or Gemini when you want top quality with no servers to run. Pick an open-weight model such as Llama, Mistral or Qwen when data residency, control or cost at high volume matter more. Hosted APIs reach production fastest. Enterprise endpoints like Amazon Bedrock and Azure OpenAI process traffic under your existing cloud agreement. Open-weight models need GPU hosting and an operations plan. In return, nothing leaves your network, and per-request cost can fall well below API pricing at steady, high volume. A model gateway in front of either choice lets you switch providers later without rewriting the app.
No. For almost every enterprise use case, custom LLM development means adapting an existing foundation model with retrieval, fine-tuning or both. Pretraining a competitive model takes trillions of tokens and large GPU clusters. Only model labs and a handful of very large companies can justify that budget. A fine-tuned open-weight model trained on a few thousand good examples can match or beat a much larger general model on a narrow task. The quality of those examples matters more than the quantity.
No method removes hallucination entirely, so we design around it: answers come from your own sources, and the system refuses when the evidence is weak. Before launch, we test against real questions with known answers. Scoring is automatic, and your domain experts spot-check the results. To protect data, we strip personal details before they reach the model and enforce document permissions at retrieval time. We also filter inputs for prompt injection. Regulated workloads can run on private endpoints or self-hosted models inside your own network.
LLM development services carry two budgets: a one-time cost to build the system and an ongoing cost to run it. The run cost often decides whether the use case pays back. Build cost depends on data prep, integration work and how much testing the risk level demands, more than on the model. Run cost is mostly tokens or GPU hours, so it grows with request volume, prompt length and model size. Routing simple requests to a smaller model and caching repeated context are the two levers that cut run cost most.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
