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Custom LLM Development Services

Build domain-specific large language models trained on your data, integrated into your systems, and engineered to work reliably in production—not just in demos. We develop secure, scalable, and high-performance custom LLM solutions that automate workflows, enhance decision-making, create measurable business value, accelerate innovation, improve operational efficiency, and support long-term enterprise growth.

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LLM Development Services

Why Generic AI Isn't Enough

Off-the-shelf models were not built for your industry. Here's why a custom approach matters.

01
Problem

Off-the-shelf models don't know your business

your business

Your clinical workflows, legal templates, and underwriting criteria aren't in GPT-4's training data. A general-purpose model gives general-purpose — and sometimes confidently wrong — answers.

02
Agitate

The risk isn't refusal — it's confident inaccuracy

confident inaccuracy

In healthcare that's a safety issue. In legal, liability. In finance, regulatory exposure. Long prompts hit context limits, and third-party APIs raise data residency questions compliance can't sign off on.

03
Solution

We build LLMs trained on what your business knows

LLMs trained

Data preparation, architecture selection, fine-tuning, evaluation, deployment, and monitoring — engineered to perform reliably in real workflows, not just in a sandbox.

Our Custom LLM Development Services

GMTA Software covers the full spectrum of large language model work — from the early strategy conversations that help you decide what to build, through to the operational support that keeps production systems performing months after go-live.

01

Domain-Specific LLM Development Domain-Specific LLM Development

Domain-Specific LLM Development

Build LLMs on proprietary domain data — clinical records, legal filings, financial documents — so the model understands your context without elaborate prompt engineering. Lower hallucination rates, correct use of industry terminology, outputs that fit your workflows.

02

LLM Consulting Services LLM Consulting Services

LLM Consulting Services
 

Architecture decisions made early are worth more than months of expensive course-correction later. We map use cases, model cost/performance tradeoffs, and give clear recommendations — not option lists that leave you guessing.

03

LLM Application Development LLM Application Development

Custom LLM Application Development

A model is not a product. We build the full application layer — conversational interfaces, internal copilots, document pipelines — with usage analytics, A/B testing hooks, and structured feedback collection built in from day one.

04

LLM Fine-Tuning Services LLM Fine-Tuning Services

LLM Fine-Tuning Services

Adapt pre-trained models (Llama 3, Mistral, GPT-4) to your task using supervised fine-tuning, LoRA, QLoRA, and RLHF. Rigorous evaluation before any model goes near production — accuracy benchmarks, latency, hallucination rates, and edge-case behavior.

05

LLM Model Integration LLM Model Integration

LLM Model Integration
 

Connect LLMs to ERP, CRM, document management systems, and internal APIs. We also build RAG pipelines that ground model responses in your internal documents, reducing hallucinations and keeping answers current.

06

LLM Support & Maintenance LLM Support & Maintenance

LLM Support & Maintenance

Performance monitoring and alerting, scheduled evaluation, fine-tuning cycle management, infrastructure scaling, and security patching — with clear SLAs defined at the start and reported against throughout the engagement.

Not Sure Where to Start? Start with a conversation.

LLM projects have more decision points than most software engagements. A 30-minute call with a GMTA engineer cuts through the noise and tells you what your use case actually needs.

View Our AI Work
LLM projects

Trusted by Teams Who Build Real Products

200+

Software & AI products delivered

30+

Industry verticals served

90%+

Client retention rate

50+

Dedicated AI & LLM engineers

AI Models, We Work With

Model selection is driven by your use case, data privacy requirements, compute budget, and deployment environment — not by which model is trending.

Model Best For Key Strength
GPT-4o
GPT-4o /
GPT-4 Turbo

Strong general reasoning, 128k token context window. Best when API access is acceptable and you need strong out-of-the-box performance with minimal fine-tuning.

Best balance of capability
Best balance of capability & ease of use
Meta Llama
Meta Llama 3 /
Llama 3.1

The most capable open-source model family for private deployment. Available in 8B, 70B, and 405B. Strong choice when data residency requirements rule out cloud APIs.
Meta Llama
Privacy First
Enterprise Ready
Mistral / Mixtral
Mistral / Mixtral

Mixture-of-experts architectures delivering high performance at lower inference cost. Well-suited for high-throughput enterprise deployments where operating cost matters.

Mistral / Mixtral
High Throughput
Cost Efficient
Anthropic Claude
Anthropic Claude 3.5 / Claude 3

Designed for structured reasoning, instruction-following, and long-document analysis. Strong for compliance-sensitive tasks where output interpretability and reduced hallucination matter.
Safer Outputs
Safer Outputs
Compliance Ready
Google Gemini
Google Gemini 1.5 Pro / Flash

Context windows up to 1M tokens. Particularly suited for long-document processing, multi-document analysis, and use cases requiring extensive conversation history.
Massive Context
Massive Context
Built for Scale
DeepSeek
DeepSeek V2 / V3

Strong performance on reasoning and coding tasks at competitive inference cost. Relevant for cost-optimized deployments in technical domains.
DeepSeek
Great for Coding
Cost Optimized
Cohere Command
Cohere Command R+

Optimized for enterprise search and retrieval-augmented generation pipelines where semantic understanding of structured documents is critical.

Enterprise Search
Enterprise Search
RAG Optimized
Phi-3 (Microsoft)
Phi-3 (Microsoft)

Smaller model family optimized for efficiency — useful for edge deployments, mobile inference, and applications where latency is more critical than maximum capability.

Phi-3 (Microsoft)
Lightweight
Low Latency

Why Choose GMTA Software as Your LLM Development Partner

Most AI vendors will tell you they work with LLMs. Fewer can tell you how they handle model drift, why they chose a specific fine-tuning approach for your data volume, or what their evaluation methodology looks like before a model goes to production.

LLM Development
01

We Engineer for Production, Not for Demos

Our engineering process is built around production requirements from day one — evaluation against real-world inputs, latency benchmarking under load, cost-per-inference analysis, and failure mode identification before deployment rather than after.

02

Deep LLM Architecture Knowledge, Not API Wrappers

GMTA engineers understand transformer attention mechanisms, tokenization behavior, context window management, inference optimization (quantization, speculative decoding), and the tradeoffs between model size and serving cost.

03

Compliance Is Built In, Not Added Later

We structure every engagement with the compliance environment in mind from the first conversation — data residency requirements, privacy regulations, access controls, audit trails, and output governance for HIPAA, GDPR, and SOC 2 environments.

04

Transparent Evaluation Methodology

Before any custom LLM goes live, it goes through benchmark scoring, human evaluation, red-teaming for adversarial inputs, latency testing under load, and comparison against your acceptance criteria. You see the results. You make the call.

05

Full Lifecycle Ownership

The team that architected your LLM system is the team that handles post-deployment optimization, retraining cycles, and infrastructure scaling. A vendor that disappears after deployment leaves you managing a system you did not build.

06

Flexible Engagement Models

Dedicated team placements for long-term AI programs, project-based engagements for defined deliverables, and staff augmentation for teams that need LLM engineering depth alongside their existing developers.

Our Custom LLM Development Process

LLM development is not a linear process. Evaluation findings often reshape architecture decisions, and production behavior frequently surfaces refinements needed in training data. We build checkpoints and iteration cycles into every phase.

PHASE
01
PHASE 01

Discovery & Research

Document use case specification, preliminary data audit, and initial architecture recommendation. We start with your business problem, not the technology.

Build from Scratch, Fine-Tune, or RAG? Here's How to Decide

RAG Is the Right

When RAG Is the Right Choice

  • Right Choice Your primary need is grounding responses in documents that change frequently.
  • Right Choice You need traceable citations back to source documents.
  • Right Choice Your knowledge base is too large to fine-tune, but well-structured enough to retrieve from.
Use Cases

Knowledge assistants, HR policy Q&A, legal document review, technical documentation search.

Fine-Tuning Delivers

When Fine-Tuning Delivers More

  • Fine-Tuning Delivers The model needs a consistent output format, or tone prompting can't reliably produce.
  • Fine-Tuning Delivers You need it to internalize domain terminology absent from pre-training data.
  • Fine-Tuning Delivers Latency is critical, and the extra retrieval step is too costly.
Use Cases

Clinical note summarization, contract clause extraction, brand-voice content generation, medical coding.

Building from Scratch

When Building from Scratch Makes Sense

  • Building from Scratch Specialized vocabulary or modality that existing models handle poorly.
  • Building from Scratch Hundreds of millions to billions of domain tokens are available.
  • Building from Scratch Full training data control required for regulatory or IP reasons.
Honest Guidance

For most enterprises, this is neither necessary nor cost-effective.

Honest Guidance
Capabilities

Types of LLM Solutions We Build

Enterprise Chatbots
01

Enterprise Chatbots & Conversational AI

Domain-specific customer support, internal helpdesks, product Q&A with multi-turn handling, session memory, fallback logic, and human escalation pathways.

OCR Extraction Automation
AI Copilots
02

AI Copilots for Internal Teams

Embedded assistants inside CRM, ERP, or document tools — drafting, summarizing, retrieving information — with role-based access controls.

Hybrid Search Embeddings Precision
Document Intelligence
03

Document Intelligence & Processing

Automated extraction, classification, and summarization from contracts, medical records, financial filings, and technical reports, with human review interfaces for high-stakes decisions.

DevTools Codebase-aware Review
Enterprise Semantic
04

Enterprise Semantic Search

Natural language search over internal document repositories and knowledge bases — hybrid search combining semantic and keyword retrieval for precision.

Analytics
Code Generation & Developer
05

Code Generation & Developer Tools

LLM tools fine-tuned on your codebase, coding standards, and internal libraries — code generation, review, test case creation, documentation.

Support RAG Guardrails
Decision Support
06

Decision Support Systems

AI that analyzes complex data and presents structured recommendations for underwriting, clinical decisions, investment analysis — with explainability and audit trails.

Productivity Integrations Context
Content Generation
07

Content Generation & Automation

Automated content pipelines trained on your brand voice and factual knowledge — marketing copy, product descriptions, compliance documents, executive briefs.

OCR Extraction Automation
Multi-Agent Workflow
08

Multi-Agent Workflow Systems

Multiple specialized LLM agents working together — retrieval, analysis, drafting, compliance review — for complex workflows a single model cannot complete reliably.

Hybrid Search Embeddings Precision
01 / 04

Not sure if you need fine-tuning, RAG, or both?

Our engineers look at your data, compliance limits, and use case, then tell you which one actually fits.

LLM projects

Hire Dedicated AI & LLM Engineers on Your Terms

The decision to engage external LLM engineering support is usually not about whether you have technical people — it's about whether you have the right mix of LLM-specific skills, and whether the economics of building that team in-house make sense for a defined project.

Full-time team

Dedicated LLM Development Team

A dedicated team assigned full-time to your LLM initiative — AI/ML engineers, prompt engineering specialist, data engineer, and project lead — embedded in your processes, working in your time zone, accountable to your delivery milestones.

Best for: Organizations building a significant LLM platform or running a multi-phase AI development program.

01
Full-time team

Staff Augmentation

Senior LLM engineers and data scientists placed alongside your existing development team — providing specialist depth on fine-tuning, RAG architecture, deployment infrastructure, or evaluation methodology — without restructuring your team.

Best for: Teams with strong general software capability needing LLM-specific expertise for a defined project phase.

02
Full-time team

Project-Based Engagement

End-to-end delivery of a defined LLM scope — from discovery through deployment and documentation — on a fixed timeline and budget. GMTA Software owns the delivery and your team owns the outcome. Best for: Organizations that want a finished, production-ready LLM system without managing the development process day-to-day.

Best for Organizations building a significant LLM platform or running a multi-phase AI development program.

03

Custom LLM Solutions Across Every Vertical

01 06

LLM Deployment Strategies:
Cloud, Private VPC, On-Premise, Hybrid

LLM Deployment Strategies

CLOUD API

Fast to deploy. Right for non-sensitive workloads.

Best for:
  • Retail and logistics use cases — product Q&A, content generation, shipment communication drafts
  • Teams running proofs of concept before committing to infrastructure
  • High-volume, low-sensitivity tasks where per-token cost is acceptable
Infrastructure GMTA manages:
  • Model selection, rate limiting, and fallback routing
  • Structured logging and cost-per-query monitoring
The real tradeoff:

Every inference call sends data to a third-party server. For retail and logistics, that's manageable with a standard DPA. For healthcare, legal, or finance—it isn't. Most providers offer enterprise tiers, but regulated data needs a different deployment model.

The real tradeoff
Key takeaway

Great speed and simplicity, but data leaves your environment.

PRIVATE VPC

PRIVATE VPC
DEPLOYMENT

Your cloud account. Your data never leaves.

Best for:
  • Healthcare, legal, and financial services where data cannot transit third-party AI providers
  • Organizations where inference volume makes per-token API pricing a meaningful operating cost
  • Any regulated use case where data residency must be enforced at the infrastructure level
Infrastructure GMTA manages:
  • GPU provisioning, vLLM or TGI serving layer, IAM access controls
  • Ongoing monitoring, patching, and retraining cycles
The real tradeoff:

Your data stays within your AWS, Azure, or GCP account. The cost of that control is operational — GPU provisioning, model serving, and ongoing management require LLM engineering depth. This is where GMTA has delivered for regulated clients across healthcare and finance.

The real tradeoff
Key takeaway

Great speed and simplicity, but data leaves your environment.

ON-PREMISE

ON-PREMISE
DEPLOYMENT

Your hardware. Fully disconnected from cloud infrastructure.

Best for:
  • Defense and government environments where data must remain on physical hardware you own
  • Organizations where regulatory obligations make cloufinance—itcessing impossible — including private VPC
  • Hard air-gapping requirements where no external network connectivity is permitted
Infrastructure GMTA manages:
  • Model selection, serving layer setup, and integration with your internal systems
  • Evaluation, security review, and handoff documentation for your infrastructure team
The real tradeoff:

Maximum data control, highest operational cost. You own every layer — hardware, power, GPU servers, model serving, and patching. For most enterprises, Private VPC meets the same compliance requirements at a fraction of the cost. On-premise makes sense when cloud-hosted processing is genuinely prohibited, not just unfamiliar.

The real tradeoff
Key takeaway

Great speed and simplicity, but data leaves your environment.

Hybrid Deployment

Hybrid Deployment (Recommended
for Most Enterprises)

Sensitive queries routed privately. Everything else is cost-optimized.

Best for:
  • Healthcare and fintech organizations running both regulated and non-regulated workloads simultaneously
  • Businesses that need compliance-grade handling for some data without paying private infrastructure costs on every query
  • Teams that want frontier model access and, where appropriate, private infrastructure where required
Infrastructure GMTA manages:
  • Sensitivity routing layer, private VPC serving, and cloud API integration
  • Unified observability across both environments — cost, latency, and compliance events in one view
The real tradeoff:

The most capable architecture is also the most complex to build. Routing logic, dual-environment observability, and fallback handling require more engineering than either option alone. For organizations where compliance and cost are both real constraints, it's the only model that resolves the tension without forcing a trade-off.

The real tradeoff
Key takeaway

Great speed and simplicity, but data leaves your environment.

Security, Compliance, and Governance Built Into
How We Work

Most LLM projects add a security review near the end, when architecture decisions have already been made and changing them is expensive. We start with your compliance requirements and let them shape the architecture from day one.

01 HIPAA
PHI de-identification pipelines, business associate agreement structures, access controls tied to care team roles, and audit logging for every data access event.
02 GDPR
Purpose limitation, data residency constraints, data processing agreements with all third-party tools in the stack, geographic restriction of processing infrastructure, data deletion capability.
03 SOC 2 Type II
System access logging, change management controls, and security monitoring built into deployment architectures. Evidence documentation available for your SOC 2 certification.
04 SEC / FINRA
Record-keeping obligations, communication surveillance requirements, and model decision explainability requirements are addressed in financial services deployments.

AI Governance & LLMOps

Output Guardrails

Rule-based and model-based filters that catch harmful, off-topic, or policy-violating outputs before they reach users. Implemented using NeMo Guardrails and custom classification layers

Frequently Asked Question

When planning a project, it is ok to have your doubts. Here are some frequently asked questions that can help you get more clarity.

When you use an API like ChatGPT or Claude, your data is sent to a third-party server, and the model was trained on general internet data. A custom LLM is trained or fine-tuned on your specific domain data, deployed in an environment you control, and adapted to your task requirements. For regulated industries or where domain accuracy is critical, a custom approach delivers measurably better results and removes data privacy concerns.

A fine-tuning engagement on a well-structured dataset can reach production readiness in 6–12 weeks. A more complex engagement involving data strategy, RAG pipeline construction, custom application development, and enterprise system integration typically runs 3–6 months. The timeline is scoped during the discovery phase based on your specific requirements and data readiness.

Not necessarily. Techniques like LoRA and QLoRA allow meaningful fine-tuning with significantly smaller datasets — sometimes hundreds to a few thousand high-quality examples. Data quality matters more than data volume. We assess your data situation during the discovery phase and recommend the approach that matches what you actually have.

Retrieval-Augmented Generation (RAG) combines an LLM with a retrieval system so relevant documents are pulled from your knowledge base at inference time. RAG suits frequent changes in information, source-cited responses, or adding new data without retraining. Fine-tuning is better when the model needs to internalize specific behavior, output format, or reasoning patterns. Many production systems use both.

Yes. We deploy open-source models (Llama 3, Mistral, and Falcon) within private cloud environments (AWS VPC, Azure Private Endpoint, and GCP VPC) and on-premise data center infrastructure. Your data never leaves your controlled environment during inference.

No LLM can guarantee zero hallucinations. We minimize the risk through RAG architectures that ground responses in verified source documents, confidence calibration and response flagging, human-in-the-loop review for high-stakes decisions, structured output formats, and systematic evaluation before deployment.

We have delivered LLM systems for organizations under HIPAA, GDPR, SOC 2 Type II, and sector-specific financial services frameworks. Compliance is addressed at the architecture level during the discovery phase — data handling, encryption, access controls, audit logging, and data residency.

Cost varies significantly based on scope. A focused fine-tuning engagement differs substantially from a full platform build with data strategy, RAG pipelines, application development, and enterprise integration. We scope and provide a transparent cost breakdown during the discovery phase before any development begins.

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Jaipur

C-305, 2nd Floor, Jan Path, Nirman Nagar, Jaipur, Rajasthan 302019

Bengaluru

Bengaluru

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Singapore

Singapore

55 Serangoon North Avenue 4 (S9) #09-01 Singapore 555859

USA

USA

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Japan

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Get in Touch

If you have questions or need assistance, we’re here to help! Feel free to reach out anytime by phone, email, or through our contact form. Let’s connect and turn your ideas into reality.

Endy Ong

Endy Ong

Insurance & Claim Management Solution

GMTA's flexible approach and prompt replies to client enquiries are among their business's assets. They have an excellent team of marketers and developers that are up to date on the latest technology and business trends.

Michelle Lim

Michelle Lim

E-Learning Solutions

GMTA Software Solutions did a really great job managing my E-learning app's development. The product is seamless and has helped me boost my business in the global market. Highly recommended!

Ong Siew Tiong

Ong Siew Tiong

Taxi Booking App

My experience with GMTA Software, developing the Li-nk app for premium limousine bookings, was exceptional. Their expertise and proactive approach ensured a top-tier product that exceeded expectations.

Kaiyang Lin

Kaiyang Lin

E-Commerce Solutions

GMTA have been a reliable partner for our project both from an analysis perspective to development and roll-out. The team has great knowledge of the service they were delivering.

Koji Matsuda

Koji Matsuda

Taxi App Development

GMTA Software developed my Tokyo MK app superbly. They understood my needs, used the latest tech, and delivered on time. Highly recommend for great app development service.

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