Large Language Model Development: Custom, Fine-Tuned and Integrated LLMs
A large language model is the engine behind chat assistants, document tools and copilots. We help you pick the right one, adapt it to your language and tasks, and run it reliably inside your products. That might mean a fine-tuned open-source model on your own GPUs, or a hosted model wrapped in your rules and data.
built for
- Product companies building on LLMs
- Enterprises that need a private, self-hosted model
- Teams working with legal, medical or financial language
- Businesses serving customers in several languages
- CTOs weighing hosted APIs against open-source models
Summarise this contract: Key terms: renewal, payment, liability and termination, each linked to its clause. (illustrative demo response)
key facts
- base models, compared on your tasks
- Open-source or hosted
- option for data that cannot leave your network
- Self-hosted
- and the Hugging Face ecosystem
- PyTorch
- testing before every launch
- Red-team
What we deliver
What you get with LLM Development. Nine parts of the service, each scoped with you.
Our LLM work covers model selection and benchmarking, fine-tuning on your examples with methods such as LoRA, retrieval over your documents, prompt design, and the serving layer that handles load, latency and cost. We work with transformer models including GPT, Llama, Mistral and Gemini, using PyTorch and the Hugging Face ecosystem.
Language models bring their own risks: bias, invented facts, harmful output and leaks of sensitive data. We test for them before launch with domain-specific evaluation sets and red-team prompts, and keep monitoring after go-live so quality does not slip as your data and the underlying models change.
- t.01
Model selection and benchmarking
We test candidate models on your own tasks and compare accuracy, speed, cost per request and licence terms before you commit to one.
- t.02
Custom domain models
Language models for specialised vocabulary in fields such as law, medicine or finance, built on an open-source base and trained on your corpus.
- t.03
Fine-tuning
Full or parameter-efficient fine-tuning (LoRA, QLoRA) on curated examples, teaching a model your format, tone and terminology.
- t.04
Retrieval and enterprise search
RAG pipelines with chunking, embeddings and re-ranking, so answers come from your current documents rather than the model's memory.
- t.05
LLM integration
Models embedded in support platforms, content management systems, CRMs and internal tools through APIs, rolled out without halting daily operations.
- t.06
Multilingual applications
Translation, multilingual chat and text generation, with quality evaluated separately in each language you serve.
- t.07
Self-hosted inference
Open-source models served on your cloud GPUs or servers with inference engines such as vLLM, using batching and quantisation to control cost.
- t.08
Evaluation and red-teaming
Automated and human-graded tests for accuracy, bias and safety, plus adversarial prompts that find failure cases before your users do.
- t.09
Monitoring and retraining
Dashboards for usage, quality and cost, alerts on errors and drift, and retraining with updated data when performance falls.
connect your sources
Connect your sources. One private index for everything your team already knows.
Your answers already exist, spread across PDFs, inboxes, tickets and ERP tables. We connect those sources, split them into chunks and index them, so a language model can find the right passage and show where it came from.
RAG vs fine-tuning- pdfDocuments & PDFscontracts · manuals · SOPs
- erpERPorders · stock · invoices
- crmCRMaccounts · deals · notes
- tktHelpdesktickets · macros · replies
- mailEmailshared inboxes · threads
- waWhatsAppcustomer chats · orders
- dbDatabasesSQL · warehouses · APIs
- xlsSpreadsheetsprice lists · trackers
- webWebsitesyour site · public pages
ingest: Documents & PDFs, ERP, CRM, Helpdesk, Email, WhatsApp, Databases, Spreadsheets, Websites → private index (chunked · embedded · searchable) → assistants · search · agents
- idx.01 · connect
Built on what you already use
We connect to the systems your team already works in, so answers come from the same records people rely on today.
- idx.02 · protect
NDA before any data moves
We sign an NDA before you share any data, and agree which sources are in scope during discovery.
- idx.03 · own
You own what we build
Once the project is paid for, the code and models we build are yours. Model and hosting costs are paid from your own account, estimated before you commit.
Why it matters
What changes once it is live. Each outcome is measured, not promised.
LLM Development is planned around these outcomes, measured against a baseline we agree with you up front.
Discuss your project- 01
A model that knows your domain
Tuning on your documents and examples teaches the model the terms, abbreviations and conventions a general model gets wrong.
- 02
Control over cost and latency
A smaller tuned model can often handle a narrow task more cheaply and quickly than the largest general model. We measure this on your workload before recommending it.
- 03
Data kept on your terms
With a self-hosted model, prompts, documents and outputs never pass through a third-party API.
- 04
No single-vendor dependence
An abstraction layer and open-source options let you move between models as prices, licences and quality change.
How we work
How we deliver LLM Development
A clear five-step process, so you always know what happens next and what you will get.
Every project starts with a free, no-obligation consultation.
$ appsinai run
Step 1: Research and design
We define what the model must do, the quality bar and the constraints, then design the architecture: hosted API, fine-tuned model, RAG or a mix.
Step 2: Collect and prepare data
Examples and documents are gathered from varied sources, cleaned of noise, duplicates and personal data, and checked for bias before training.
Step 3: Train and fine-tune
We adapt the chosen base model with PyTorch on GPU infrastructure, logging each run so results can be reproduced and compared.
Step 4: Evaluate and test for safety
The model is tested on domain tasks, bias and harmful-output checks and red-team prompts, and compared with the base model and alternatives.
Step 5: Deploy and monitor
We deploy behind an API with logging, rate limits and dashboards, then track quality and retrain as data and requirements change.
↺ results feed the next improvement cycle$
Security & control
LLMs built for sensitive data
When a language model reads contracts, patient notes or financial records, where it runs and what it retains matter as much as how well it writes.
Ask us about security- 01
Private deployment
Open-source models can run entirely inside your VPC or data centre, in the region you choose, with no calls to an outside API.
- 02
Clean training data
Personal and confidential details are removed or masked from fine-tuning data, reducing the risk that the model repeats them in answers.
- 03
Guardrails and red-team testing
Input and output filters, refusal rules for out-of-scope requests and adversarial testing for prompt injection and data leakage.
- 04
NDA and model ownership
We sign an NDA before you share any data. On completion and final payment, fine-tuned weights trained on your data are handed over, as set out in the contract, subject to the base model's licence.
FAQ
Questions about LLM Development
Straight answers to what buyers ask us most. Can't find what you need?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyDo we need our own LLM, or can we use a hosted model like ChatGPT?
Many businesses do well with a hosted model plus retrieval over their own documents. Your own fine-tuned or self-hosted model makes sense when data cannot leave your network, when request volumes make API costs high, or when you need behaviour a hosted model will not reliably produce. We test both routes on your tasks before recommending one.
ChatGPT & OpenAI integrationWhich large language models do you work with?
Hosted models such as OpenAI GPT, Google Gemini and Anthropic Claude, and open-source models such as Llama and Mistral. We also build hybrid systems that combine an LLM with classic machine learning, for example a classifier that routes requests before the language model answers.
What does LLM development cost?
Cost depends on the base model and its size, the amount of training data to collect and clean, GPU time for training, hosting and serving, and the integration work. Using a hosted API with retrieval is usually cheapest to start; a self-hosted fine-tuned model costs more up front but can cost less per request at volume.
Estimate your LLM project costHow much data is needed to fine-tune an LLM?
For a narrow task such as a fixed output format or a classification style, a few hundred to a few thousand high-quality examples is often a workable starting point. Quality matters far more than volume. Teaching a model a whole new domain takes much more text, and we tell you after a first trial whether your data is enough.
Can the model run on our own servers?
Yes. Open-source models can be hosted on your own GPUs or cloud account. We size the hardware to the model and expected traffic, and use quantisation where it keeps quality acceptable at lower cost.
How do you test an LLM for accuracy and bias?
We build an evaluation set from your real tasks with expected answers, score the model automatically and with human reviewers, run bias and harmful-output checks across sensitive topics, and try adversarial prompts. The same set is re-run after every change so you can see whether quality went up or down.
What ongoing support do you offer for LLMs?
Monitoring of outputs for errors, bias and drift, retraining with new data, prompt and retrieval adjustments, usage and cost dashboards, upgrades when better base models appear, and capacity planning as traffic grows.
Free PDF brochure
LLM Development brochure
Features, outcomes, delivery process and FAQs in one PDF you can share with your team or management.
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related
Keep exploring
- Model Training & Fine-TuningDataset preparation, LoRA fine-tuning and evaluation to fit a model to your terminology and formats.
- RAG vs fine-tuningWhich approach suits your LLM project, and when it pays to combine them.
- ChatGPT & OpenAI IntegrationGPT models connected to your apps and data through the OpenAI API, with usage and data controls.
- AI & ML DevelopmentEnd-to-end AI and machine learning development, from a feasibility check and proof of concept to models running inside your ERP, CRM or apps.
- Generative AI DevelopmentGenerative AI applications that draft, summarise, search and answer from your own content, built on the model that fits and tested before rollout.
- AI Agent DevelopmentAI agents that read, decide and act across your systems, completing multi-step tasks with human approval where it matters and a log of every step.
- AI Chatbot DevelopmentCustomer-facing AI chatbots on your website, WhatsApp and app that answer from your content, complete simple tasks and hand difficult conversations to your team.
- Machine Learning DevelopmentCustom machine learning models for forecasting, risk, recommendations and inspection, trained on your own data and evaluated before they reach production.
- Computer VisionCustom computer vision models that detect, count, read and inspect what your cameras see, trained on your own images and run in the cloud or on edge devices.
Free consultation
Find the right language model for your use case
Book a free 30-minute call. Share your use case and data constraints, and we will suggest which models to benchmark, where to host them and whether fine-tuning is worth it.