AI-ML
Global Services
Custom LLM Development Services for Your Proprietary Data
500+
Projects Delivered
98%
Client Retention
Certified by:
HIPAA
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ISO 27001
Trusted By Industry Leaders
Certifications and Accreditations
Why Domain-Specific Workflows Need Custom LLMs
A public model answers general questions well and enterprise-specific questions badly. It doesn’t know your product taxonomy, your internal shorthand, or the difference between two clauses that look identical to a general-purpose model but mean very different things in your contracts.
Custom LLM development services exist because that gap doesn’t close on its own. Teams that fine-tune on their own documents, or build a retrieval layer over their own data, see fewer corrections and outputs their staff actually trust. Teams that keep prompting a generic API harder usually just get more consistent versions of the same wrong answer.
Not Sure Which Approach Fits Your Data?
Get a 30-minute scoping call before you commit to a build direction.
Talk to Our LLM DeveloperCustom LLM Development Services Built Around How You Actually Operate
Custom LLM development services only earn their cost if every stage, from strategy through post-launch monitoring, is handled by people who have shipped models to production before, not just fine-tuned one in a notebook. Citrusbug covers the full cycle so nothing gets handed off to a separate vendor mid-project.
LLM Consulting and Use Case Strategy
Our LLM consulting starts with mapping your data landscape, ranking viable use cases by impact and feasibility, and identifying the architecture that can deliver them reliably.
Data Engineering and Annotation
We clean, structure, and annotate domain-relevant datasets from support tickets, internal documentation, or structured records so the data engineering work that gets those documents structured is done right the first time.
Custom Model Training and Fine-Tuning
Using LoRA and QLoRA on open-source bases, or fine-tuning layers on top of hosted models, we adapt a base model to your vocabulary and reasoning patterns without retraining from scratch.
RAG Implementation
For teams whose real need is accurate answers from a large document corpus, we design the chunking strategy, embedding pipeline, and retrieval logic that make a model useful in production.
Model Evaluation and Safety Testing
Every model is benchmarked against held-out domain test sets and checked for hallucination rate, bias, and safety before it ever touches production traffic.
Secure Deployment
Private cloud, on-premises, or hybrid deployment keeps training data and model weights inside environments you control, never a shared third-party inference layer.
System Integration
Secure APIs connect the model to your CRM, ERP, or internal knowledge base, so it operates inside workflows your team already uses instead of a standalone tool nobody opens.
Monitoring and Post-Launch Support
We track accuracy, latency, and drift after launch, and define retraining triggers so the model doesn’t quietly degrade as your data changes.
How to Choose Between Fine-Tuning RAG and Full Model Training
Most vendors sell whichever approach is more billable, not whichever one actually fits your problem. Citrusbug's discovery phase produces a written recommendation on fine-tuning, RAG, or full custom training, including realistic cost and timeline tradeoffs for each, before you sign anything. That decision often points toward the cheaper option. We'd rather you know that upfront than find out after a six-figure build.
Fine-Tuning
Best when your model already answers correctly most of the time but misses your terminology or tone. We adapt an existing base using LoRA or QLoRA on curated examples from your domain, launching in weeks rather than months.
RAG
Best when your real need is accurate, current answers pulled from a document corpus that keeps changing. This is often the faster and cheaper starting point, and it’s where we’d rather fine-tune the model on a curated retrieval layer instead of jumping straight to full training.
Full Custom Training
Best for companies with large proprietary datasets and performance requirements no public model can meet, where architectural control and long-term model ownership matter more than speed to launch.
Hybrid Approach
Some engagements combine a fine-tuned reasoning layer with a retrieval pipeline underneath it, giving you both domain-adapted reasoning and access to documents that change weekly.
The Architecture Decisions Behind a Production-Ready Custom LLM
A model that performs well in a demo and one that holds up under real production traffic are built differently. The architecture choices below, made during discovery rather than guessed at mid-build, determine whether your model stays accurate, affordable to run, and auditable as your data changes.
- Parameter-efficient fine-tuning with LoRA and QLoRA
- Private cloud or on-premises inference deployment
- Vector database retrieval for grounded, current answers
- Continuous evaluation and drift monitoring pipelines
- Secure API layer for enterprise system connections
Integration Capabilities That Connect Your Model to Systems You Already Run
A model that can't reach your CRM or your internal knowledge base is a research project, not a business tool. Most of the actual engineering effort in a custom LLM engagement goes into system integration planning that maps every connection point in advance, not the fine-tuning step itself.
We handle authentication, rate limiting, and data flow mapping for each connection so nothing breaks the first time it hits real traffic volume.
We handle authentication, rate limiting, and data flow mapping for each connection so nothing breaks the first time it hits real traffic volume.
CRM Systems
Salesforce, HubSpot, and internal CRMs connect through secure APIs so the model can read and act on live customer context.
ERP and Data Warehouses
Structured records from SAP, NetSuite, or Snowflake feed the model without duplicating data into a separate system.
Internal Knowledge Bases
Confluence, SharePoint, or internal wikis become queryable sources instead of static documents nobody searches.
Customer Support Platforms
Zendesk and similar tools get model-generated draft responses and routing logic without replacing the agent workflow your team already knows.
How Much Does It Cost to Develop a Custom LLM?
Most engagements land between $25,000 for a focused fine-tuning project and $150,000 or more for full custom training, depending on data readiness and scope.
Tell us about your use case and we'll scope it accurately.
Related Projects We’ve Built
How Citrusbug Builds and Deploys Custom Large Language Models
1
Discovery and Use Case Definition
We define the specific problem the model needs to solve, audit what data you already have, and rank use cases by feasibility and business impact. This phase ends with the fine-tune versus RAG versus full-training recommendation, not a generic proposal.
2
Data Engineering and Annotation
Raw documents, tickets, and records get cleaned, deduplicated, and annotated into training-ready datasets. This step is consistently underestimated on other vendors' timelines and consumes real effort here.
3
Architecture and Base Model Selection
We select an open-source base like Llama or Mistral, or a hosted model where API-based tuning fits better, based on your latency, cost, and data governance requirements rather than a default preference.
4
Fine-Tuning and Training
Using LoRA, QLoRA, or full supervised fine-tuning depending on the chosen approach, we run iterative training cycles with domain expert review between rounds until outputs meet your target metrics.
5
Evaluation and Safety Testing
The model is benchmarked against held-out domain test sets, checked for hallucination rate and bias, and red-teamed before anyone outside the project sees production output.
6
Integration and Deployment
The model ships behind secure APIs into your existing systems, on infrastructure sized for your actual query volume rather than a default configuration.
7
Monitoring and Retraining
We set up drift and accuracy monitoring with defined retraining triggers, so the model stays aligned with your data instead of quietly drifting six months after launch.
What You Own When the Engagement Ends
Full source code and training pipeline
The fine-tuned model artifact and evaluation report
Deployment and infrastructure configuration
API documentation and integration guides
Monitoring dashboard and retraining playbook
NDA-protected IP transferred at delivery
Engagement Models
What Custom Large Language Model Development Actually Costs
The cost of custom LLM development services depends on how much of the model stack needs to be built from scratch. An existing model may only need domain adaptation, while more specialized use cases can require custom training, data preparation, and dedicated infrastructure.
Fine-Tuning Only
Adapt an existing base model to your domain and vocabulary.
- Typical range: $25,000 to $80,000
- 8 to 12-week timeline
- LoRA or QLoRA on an open-source base
- Best when a general model is close but not precise enough
- Lower infrastructure footprint to maintain
- Fastest path to a production-ready model
RAG Plus Integration
Build a retrieval layer over your live document corpus.
- Typical range: $80,000 to $150,000
- 10 to 14-week timeline
- Vector database and embedding pipeline included
- Best when your data changes faster than a model can be retrained
- Full system integration into CRM or knowledge base
- Ongoing indexing as new documents arrive
Full Custom Training and Ownership
Train and own a model built entirely around your data.
- Typical range: $150,000 and up
- 4 to 6-month timeline
- Full architectural control over the base model
- Best when public models can't meet performance requirements
- Complete source code and training pipeline delivered
- Long-term iteration control stays with your team
Meeting Compliance Requirements Without Slowing Delivery
The EU AI Act’s high-risk system obligations take effect in August 2026, and most healthcare and fintech buyers are already asking vendors how a model’s training data and outputs get documented, not just whether it works.
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HIPAA-aligned architecture for clinical and health data
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SOC 2 Type II control mapping from day one
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Audit trails built for high-risk AI system documentation
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Security and compliance work that runs alongside the build, not bolted on after
Why Build a Custom LLM Instead of Relying on Prompts?
Per-query costs drop once inference runs on infrastructure you control instead of metered API calls billed by the token.
Outputs stay consistent across teams because the model doesn't silently change behavior with every vendor update.
Manual review time falls once the model already understands your terminology and internal process language.
Data governance gets simpler when nothing leaves your environment during training or inference.
Client Testimonials (We're Rated 4.7 on Clutch)
THE PROJECT
Web & AI Development for Education Company
AI Development, Custom Software Development
Nov. 2024 - Ongoing
THE REVIEW
Their agile way of working, competence, and friendliness are impressive.December 7, 2024
PROJECT SUMMARY:
Citrusbug Technolabs handles a data extraction project for an education company. They're also responsible for managing an AI generation project for the client.
FEEDBACK SUMMARY:
Citrusbug Technolabs has laid out and followed a clear project outline and consistently met deadlines. The team communicates well and demonstrates a strong understanding of the client's vision and goals. Their agile approach, high level of competence, and personable approach stand out.
THE REVIEWER
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THE PROJECT
Custom Automated System Dev for Veteran Medical Co
Generative AI, Application Testing, Custom Software Development
Aug. - Oct. 2024
THE REVIEW
Their communication and ability to capture exactly what I was asking for were impressive.November 14, 2024
PROJECT SUMMARY:
Citrusbug Technolabs built an automated system for a veteran medical company. The system needed to collect and store user data, generate customized letters based on surveys, and use AI to summarize reviews.
FEEDBACK SUMMARY:
Citrusbug Technolabs delivered a functional automated system that successfully organized the client's records and auto-generated statements. The team was quick to respond, addressed all roadblocks, and communicated effectively. Overall, their ability to understand the client's needs stood out.
THE REVIEWER
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THE PROJECT
MVP Development for Freight & Capacity Marketplace
Custom Software Development
$10,000 to $49,999
Jan. 2024 - Feb. 2025
THE REVIEW
They were very calm and talented, and they did bring some of their own ideas.January 28, 2025
PROJECT SUMMARY:
Citrusbug Technolabs has developed an MVP for a freight and capacity marketplace. The team has created three different interfaces for three market segments.
FEEDBACK SUMMARY:
Citrusbug Technolabs has successfully developed an effective MVP. The team has delivered the project on time and almost within budget. Moreover, their responses have been timely. Overall, the team has been calm, talented, and innovative. This has led to the client's satisfaction.
THE REVIEWER
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THE PROJECT
Web & Mobile App Development for Education Company
Custom Software Development, DevOps Managed Services
$10,000 to $49,999
THE REVIEW
What stood out most about them was their personalized approach and sense of ownership in every aspect of the project."February 27, 2024
PROJECT SUMMARY:
Citrusbug Technolabs built a Flutter cross-platform mobile app for an education company. They also developed two web apps and an admin panel and provided DevOps support for the client's infrastructure.
FEEDBACK SUMMARY:
Citrusbug Technolabs' support successfully boosted the client's user engagement by 8x, resulting in an 85% retention rate. The reliable team demonstrated a dedication to delivering on time and promptly responding to concerns and adjustments. Their personalized, client-centric approach stood out.
THE REVIEWER
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THE PROJECT
Mobile App Development for Healthcare Company
Custom Software Development Mobile App Development UX/UI Design
$10,000 to $49,999
June 2023 - Jan. 2024
THE REVIEW
They didn't like the phrase "impossible to do", which made them very solution-oriented.February 19, 2024
PROJECT SUMMARY:
A healthcare company hired Citrusbug Technolabs to design and develop a user-friendly application. The team provided tailored assistance to meet the client's requirements.
FEEDBACK SUMMARY:
Citrusbug Technolabs successfully delivered an application that was ready for market. The team was highly professional and well-organized; they stayed focused throughout the engagement and were receptive to changes. Overall, they stood out for their trustworthiness and commitment to excellence.
THE REVIEWER
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THE PROJECT
Custom Web App Dev & Design for Financial Technology Firm
Mobile App Development UX/UI Design Web Design
Sep. 2023 - Ongoing
THE REVIEW
They're 100% on time and transparent throughout each stage of the project.February 24, 2024
PROJECT SUMMARY:
A financial technology company has hired Citrusbug Technolabs to build a custom web application. The team handles the development and design of the app.
FEEDBACK SUMMARY:
The project is ongoing, but the client has been satisfied with Citrusbug Technolabs' services. The team is very professional — they always respect timelines and provide regular updates to ensure everyone's on the same page. Also, their transparent approach makes them a trustworthy partner.
THE REVIEWER
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THE PROJECT
Staff Augmentation for Singaporean Development Company
Mobile App Development
$10,000 to $49,999
THE REVIEW
"I do believe they are exceptional at where they stand."
PROJECT SUMMARY:
Citrusbug Technolabs took on a mobile app development project for a Singapore-based development house. They were provided with UX designs to execute. Currently, they're finalizing and fine-tuning the product.
FEEDBACK SUMMARY:
Citrusbug Technolabs contributed to the successful launch of the product, which has garnered over 10,000 users in just three months. The product has also received support from investors. They consistently followed-up with their progress and delivered at a speedy rate. The team is truly stellar.
THE REVIEWER
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Why Citrusbug
Why Choose Citrusbug for Custom LLM Development Services
Discovery Before Any Line of Code
We document the fine-tune, RAG, or full-training recommendation with real cost and timeline tradeoffs before any contract is signed, not after.
Senior AI Engineers Only
You know who is building your model before you sign, not after a resourcing swap three weeks into the engagement.
Evaluation Set Before Go-Live
Every model is benchmarked against a held-out domain test set with documented pass criteria, not shipped on the strength of a good demo.
SLA Options After Launch
Post-launch monitoring and retraining support are available as ongoing options, not a one-time handoff that leaves drift unmanaged.
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What is the difference between fine-tuning and RAG for our use case?
Fine-tuning changes how the model reasons about your domain. RAG retrieves facts from your documents at query time. Most enterprises start with RAG and add fine-tuning later.
How long does a custom LLM engagement take from kickoff to production?
Fine-tuning and RAG projects typically reach production in 8 to 14 weeks. Full custom training runs 4 to 6 months depending on data volume and evaluation requirements.
Do we own the model and training pipeline after delivery?
Yes. You receive full source code, the trained model artifact, training pipeline, and documentation under NDA. Nothing stays locked to Citrusbug after handover.
Can the LLM run entirely on our own infrastructure?
Yes. We deploy on a private cloud, on-premises servers, or a hybrid setup depending on your data governance and latency requirements.
How do you prevent the model from hallucinating on our data?
We ground outputs in your verified data through retrieval pipelines and domain-specific fine-tuning, then validate against held-out test sets before any production rollout.
What happens to the model as our data and processes change?
We set up monitoring for drift and accuracy, then define retraining triggers so the model stays aligned with your current data and workflows.
Which LLM base models do you fine-tune?
We work with Llama, Mistral, and other current open-source bases, plus fine-tuning layers on top of hosted models where API-based tuning fits better.
How do you handle compliance for healthcare or financial data?
Training and inference run inside HIPAA-aligned or SOC 2 mapped environments with encryption, access controls, and audit trails documented for regulatory review.
What do we need to provide before you can start?
Domain documents, historical support tickets, or structured data relevant to your use case. We help with cleaning and annotation if the data isn't ready yet.