LLM development services cover the design, fine-tuning, integration and deployment of large language model applications that work on your company's data. SDLC Corp builds custom LLM solutions with RAG, fine-tuning and private deployment, from the first use case to production. Our clients are enterprises, government bodies and product companies that need LLMs inside their own systems.
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Google AIAWSGoogle CloudMicrosoft AzureNVIDIAOpenAIAnthropicGeminiGrokPerplexityGoogle AISDLC Corp's LLM development services take a large language model from use case to a production system your team owns. Most projects combine two or three of the services below.
LLM consulting finds the use cases where a language model gives a measurable return, then sets the data, model and budget plan. As a result, you start with one measurable pilot instead of a broad AI program.
Custom LLM development builds copilots and assistants for your workflows, or LLM features inside your own SaaS product. For example, an assistant can answer ERP questions in plain English while each user keeps their existing access rights.
LLM chatbot development builds support and sales chatbots that answer from your knowledge base and escalate complex cases to staff. In addition, the chatbot logs every conversation, so your team can review answers and improve them.
LLM fine-tuning trains a model on your labeled examples, so it follows your formats, tone and domain terms. We use efficient fine-tuning methods such as LoRA when full retraining is too costly.
RAG integration indexes your documents so the model can retrieve approved information and cite its sources. Our RAG development services team handles indexing, permissions and retrieval quality.
LLM integration places the model inside the software you already run, such as Odoo, Salesforce, SAP or a custom portal. For rollouts across several ERP and CRM systems, see our AI integration services .
We deploy on AWS, Azure or Google Cloud, or as a private deployment when data must stay inside your network. After launch, the same team monitors the system and handles model updates.
Each industry gives a model a different kind of data. Therefore, the right approach and guardrails change with the use case.
Banking · Insurance · Lending
Claims and policy assistants answer staff questions from policy wordings, claim files and underwriting rules. In addition, they summarize long claim histories before a person makes the decision.
Read MoreHealth · Life Sciences
Clinical note summaries and intake assistants work on patient records under strict access control. Health data stays in a private environment, and a clinician reviews every output.
Read MoreFreight · Supply Chain
The model reads freight invoices, bills of lading and carrier emails, then maps each charge to a shipment. Approved lines then post to the TMS or ERP through its API.
Read MoreIndustrial · Plant Operations
Assistants answer questions about machine data, orders and quality reports in plain language. For instance, a plant manager can ask for last week's scrap rate by production line.
Read MoreCommerce · Ecommerce
Models write product content, enrich catalogs and draft support replies from your product data and policies. Meanwhile, agents edit each suggested reply before they send it.
Read MoreGovernment · Citizen Services
Citizen service assistants answer questions from published rules, forms and guidance. Every answer cites its source, and complex cases go to a staff member.
We start by testing prompts and your data. RAG connects an LLM to current business information, while fine-tuning adapts model behavior. Many systems combine both based on accuracy, cost and deployment needs.
| Aspect | RAG | Fine-Tuning | Both |
|---|---|---|---|
| Best for | Answers from documents and records that change | Consistent formats, tone and domain-specific tasks | Domain assistants needing current facts and consistent responses |
| Data needed | Source documents and retrieval test examples | Task-specific training examples | Source documents and training examples |
| Keeps knowledge current | Yes, when sources and indexes are updated | Not automatically; model updates may require retraining | Yes, through updated retrieval sources |
| Relative cost | Depends on indexing and retrieval usage | Depends on training and inference costs | Includes training and retrieval costs |
| Time to first release | Often faster with ready data | Depends on data and training scope | Depends on training and integration scope |
Our LLM development process runs in six stages. Each stage ends with an output your team reviews before the next one starts.
We define the use case, users, data sources, accuracy target and budget.
OutputUse case brief and success metrics
Next, we gather approved documents and records, then clean, deduplicate, chunk and label them.
OutputClean dataset and data map
We compare candidate models on your data for quality, cost and latency, then set up RAG or training.
OutputModel choice with test scores
Where RAG alone falls short, we fine-tune on labeled examples and validate against a held-out test set.
OutputTuned model and validation report
Then we test accuracy, hallucination rate, security and permissions against real business questions.
OutputEvaluation results and sign-off
Finally, we release in stages and track answer quality, latency and user feedback in production.
OutputLive system, dashboards and a named owner
We choose LLM models based on answer quality, cost, privacy and deployment needs, rather than relying on one vendor.
OpenAI Claude Gemini
Llama
Mistral
Python
PyTorch
Hugging Face
LangChain
n8n
PostgreSQL
MongoDB
Snowflake
Databricks AWS Azure Google Cloud
Docker
KubernetesBefore release, we evaluate LLM performance on your business data. Our AI governance consulting team helps define risk ownership, approval criteria and responsible AI controls.
Before launch, we test real business questions against expected answers. Each model or prompt change is checked for accuracy, unsupported claims and retrieval quality, so release decisions rely on measured results.
Input and output guardrails help detect prompt injection, off-topic requests and unsafe answers. These checks address risks in the OWASP Top 10 for LLM Applications. Flagged or low-confidence answers can be routed for human review.
We minimize or mask sensitive data where appropriate, while retrieval respects each user's access permissions. For each project, we select model endpoints and settings based on agreed privacy, retention and data-use requirements.
We configure audit logging for relevant activity, with privacy and retention controls. Our approach draws on the NIST AI Risk Management Framework and considers applicable EU AI Act and industry requirements.
We track cost per request and response time for each workflow. Caching, smaller models and prompt optimization can control spending while maintaining the answer quality each use case needs.
We apply experience from our own AI products to client LLM projects, combining model evaluation, enterprise integration and production monitoring.
We build and operate Data AI Ninja for document processing and Pulastya AI voice agents, bringing practical product experience to client LLM development.
At the same time, we select models based on quality, cost and deployment needs, keeping prompts and integrations portable where practical to reduce vendor dependence.
Our ISO 9001:2015 and ISO/IEC 27001:2022 certifications support structured quality and security practices. Review certification details in our Trust Center.
Clients retain ownership of custom code and project IP under the agreed contract, with clearly defined deliverables, documentation and handover terms.
Explore LLM and AI projects integrated with real business systems, including ERP assistants, workflow automation, freight intelligence and voice agents.
Employees ask plain-English questions and get answers from live Odoo records, within their existing role permissions.
GPT-4o, LangChain and pgvector power workflows for website enquiries, sales follow-ups and management reporting.
Azure Document Intelligence reads freight invoice emails, and GPT matches each charge to a cost code. Approved lines then post to Oracle OTM.
A voice agent verifies callers in the CRM, looks up live ERP orders and opens prioritized ServiceNow tickets.
Real client experiences highlight how SDLC Corp transforms complex requirements into reliable solutions that support business growth.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, Roofaid USA
Reyzal Razmi
All Star Influencers
They've quickly understood our business context, moved fast, and brought recommendations rather than questions.
What stood out was their ability to translate our broad operational challenges into clear system features without overcomplicating the solution.
What impressed us most about SDLC Corp was their ability to understand our clinical requirements and translate them into practical technology solutions.
The predictive shipping models they implemented have saved us both time and cost while improving our accuracy and customer satisfaction.
Their data-driven methodology and exceptional command of artificial intelligence made a significant impact on our business operations.
Share your use case and data sources. Our engineers will scope the approach and first release.
Find out what LLM development includes and how we choose the right model and approach. We also explain data security, testing, cost, timelines and support after launch.
LLM development services build applications on large language models, from selecting a use case through production. Work includes data preparation, model selection, RAG or fine-tuning, integration, evaluation, deployment and monitoring. The result is an LLM application connected to your business data and workflows.
LLM development focuses on language-based applications such as assistants, enterprise search and document processing. Generative AI development is broader, covering image, audio and video generation as well. For projects beyond language workflows, explore our generative AI development services.
Choose RAG when answers need current information from your documents or systems. Fine-tuning can improve consistent formats and task-specific behavior when prompting alone falls short. Some applications use both. We evaluate answer quality, available data and cost before recommending an approach.
Rarely. Training a large language model from scratch requires substantial data, computing resources and investment. For most business use cases, we adapt established models such as GPT, Claude, Llama or Mistral using RAG, prompting or fine-tuning. This is generally faster and more cost-effective than training a foundation model.
We work with OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama and Mistral models. Selection depends on your use case, data security, quality targets, latency and budget. Open-weight options such as Llama or Mistral can also support private deployments when the model license and infrastructure allow.
We use agreed data environments and minimize or mask sensitive information where appropriate. Access controls, retention periods and model-provider data settings are defined for each project. Our information-security management practices are supported by ISO/IEC 27001:2022 certification.
Before launch, we test representative business questions against expected answers. We measure answer accuracy, unsupported claims, citation quality, latency and cost per request. Model and prompt changes are retested against agreed evaluation criteria, and production monitoring helps detect changes in quality after launch.
Timelines depend on data readiness, fine-tuning needs, integration complexity and approval requirements. A RAG pilot using one prepared document set can launch sooner than an enterprise assistant connected to several systems. After discovery, we provide a stage-by-stage delivery estimate based on the agreed scope.
Our Clutch profile lists a $10,000+ company-wide minimum project size and a $25–$49 hourly range. These are not LLM-specific quotes. For planning, LLM projects may fall into three indicative budget ranges, depending on scope, integrations and deployment needs:
Model usage, hosting and ongoing support may add recurring costs. We estimate these separately and provide a project-specific quote after reviewing the requirements.
Yes. Our support can include monitoring, prompt and index updates, issue resolution and evaluation of newer models before release. The agreement defines maintenance scope, documentation, response arrangements and the responsible support contact.
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