An AI that knows your business as well as your best employee
Its brain is a Large Language Model, trained on your company’s data - so your team
can search, ask, decide, and work with business-specific intelligence.
25+
Knowledge Sources Connected
1
Private Business Knowledge Layer
3w
Model Prototype Timeline
24/7
Access to Company Knowledge
Custom LLM Model Development
Business oriented
LLM development
Instead of a generic AI tool that gives generic answers, your team gets a model that understands your business context: policies, products, processes, customer data, internal documents, and the way your company actually works.
LLM Model Problems We Fix
Company knowledge is scattered everywhere?
Documents, SOPs, Slack threads, CRM notes, PDFs, and spreadsheets become one searchable AI knowledge layer.
Your team asks the same questions again and again?
A custom LLM can answer repetitive internal questions using approved company data and context.
Generic AI tools do not understand your business?
Your model is grounded in your terminology, workflows, products, services, and internal logic.
Important knowledge lives in people’s heads?
We help turn expert knowledge into a structured AI system your whole team can access.
Internal search is slow and unreliable?
Instead of digging through folders and documents, teams can ask questions and get direct answers.
Support teams repeat the same explanations?
Custom LLMs help answer product, policy, onboarding, and customer questions faster.
New employees take too long to onboard?
Company knowledge becomes easier to find, understand, and apply from day one.
Sensitive data cannot go into generic AI tools?
Private LLM setups can be designed around access control, permissions, and safer data handling.
You need AI that follows your business rules?
The model can be configured around approved sources, workflows, review steps, and escalation logic.
Full-service Custom LLM Development
Whatever your business knows - we turn it into a private AI knowledge system.
LLM Strategy & Data Readiness
The best model starts with the right data. We review your documents, systems, knowledge sources, workflows, and business goals to define what the LLM should know and where it should create value first.
Private Knowledge Base Architecture
Company knowledge becomes structured, searchable, and ready for AI retrieval. Documents, policies, PDFs, spreadsheets, CRM notes, and internal materials are organized into a reliable knowledge layer.
RAG System Development
Answers are generated from your approved business sources, not from generic assumptions. Retrieval logic helps the model find relevant context before producing a response.
Custom LLM Fine-tuning
When your business needs more specific behavior, tone, terminology, or classification logic, fine-tuning helps align the model with your domain and expected outputs.
Internal AI Assistants
Teams can ask natural-language questions and get answers from internal company knowledge. Useful for onboarding, operations, sales enablement, support, HR, and process documentation.
Customer Support LLMs
Support teams get faster access to product, policy, and customer-facing knowledge. The model can draft answers, suggest next steps, and route complex questions for human review.
LLM Integrations & Interfaces
A custom LLM should work where your team already works. We connect it to Slack, CRMs, internal platforms, dashboards, customer support tools, or custom interfaces.
Security, QA & Monitoring
Reliable LLM systems need boundaries. We set up permissions, approved sources, fallback logic, testing flows, monitoring, and review points before the system becomes part of daily work.
How We Work
Discovery & Knowledge Audit
- Business goals and LLM use case alignment
- Company data and document source audit
- Workflow, user role, and access review
- Knowledge gaps and risk areas
- Technical approach recommendation
Data Architecture & Model Planning
- Knowledge base structure
- Data cleaning and source preparation
- Retrieval logic and model behavior planning
- Permission and review flow design
- Prototype specification
Prototype & Model Setup
- LLM prototype development
- Knowledge base or RAG setup
- Prompt logic and response rules
- Interface or integration setup
- First test version for internal review
QA, Testing & Refinement
- Answer quality testing
- Hallucination and edge case checks
- Source accuracy validation
- Permission and access testing
- Prompt, retrieval, and output improvements
Launch & Handover
- Production deployment
- Team training and usage documentation
- Admin handover
- Monitoring setup
- Ongoing model improvement available
Case Studies
Results that Compound
Built ML that Underwrites a Mortgage in Seconds
Two Canadian banks were issuing mortgages in weeks. We built the ML system that automated underwriting end to end.
In Their Words
We’ll turn your documents, workflows, and internal expertise into a custom LLM your team can actually use.
Got company knowledge your team cannot access fast enough?
Industries We Build For
We build custom LLM models for teams that need business-specific answers, private knowledge systems, and faster access to company data.
Financial Services
Custom LLMs for policy search, compliance support, document review, internal knowledge access, customer context, and operational decision support.
SaaS & Software
Private AI assistants for product documentation, onboarding, customer success, support workflows, internal knowledge bases, and sales enablement.
Real Estate & PropTech
LLM systems for property data, market research, broker workflows, listing knowledge, client communication, and internal document search.
Rentals & Property Management
Custom knowledge models for booking rules, property operations, maintenance workflows, client support, location data, and team documentation.
Crypto & Web3
LLMs for technical documentation, community support, product education, internal reporting, token knowledge, user questions, and fast-moving team operations.
E-commerce & DTC
AI knowledge systems for product data, customer support, order policies, inventory logic, campaign materials, and retention workflows.
Healthcare & MedTech
Private LLMs for reviewed internal knowledge, patient communication support, admin workflows, medical product documentation, and controlled information access.
Professional Services
LLMs for agencies, consultancies, legal teams, accounting firms, and service businesses that need faster access to client, process, and project knowledge.
Education & EdTech
AI assistants for course knowledge, student support, admissions workflows, internal documentation, training materials, and knowledge base automation.
Why Now
Every month without a private AI knowledge system is another month your team keeps searching, asking, copying, and repeating work manually.
Your company knowledge is growing faster than your systems
Documents, messages, files, tools, and internal processes keep expanding. Without a smarter way to access them, teams lose time finding information they already have.
Generic AI cannot understand your business
Public AI tools can help with general tasks, but they do not know your customers, workflows, policies, terminology, or internal decisions unless that context is built in.
Your best employees should not be the search engine
When expert knowledge lives in a few people’s heads, everyone depends on them for repeated answers. A custom LLM makes that knowledge easier to access across the team.
Faster answers create faster operations
Support, sales, onboarding, reporting, and internal coordination all move faster when teams can ask questions and get company-specific answers immediately.
AI adoption needs structure
Teams are already trying AI manually. A custom LLM gives them a controlled, business-specific system instead of disconnected prompts and inconsistent outputs.
Tell us what your AI should know -
we'll take it from there
No commitment required to start. Share your documents, workflows, tools, and business goals. We’ll identify what can become a custom LLM, what should be handled through RAG, and what needs extra security or human review.
Book a Strategy Call
Frequently Asked Questions
Questions, Answered
What is a custom LLM model?
A custom LLM is an AI system adapted to your company’s data, documents, workflows, terminology, and business context. It can answer questions, search knowledge, summarize information, and support internal processes.
Do you train the model on our company data?
Yes. Depending on the use case, we can use retrieval-based systems, private knowledge bases, RAG, fine-tuning, or a combination of approaches to make the model work with your company data.
What kind of data can the LLM use?
The model can work with documents, PDFs, SOPs, policies, product information, CRM data, support materials, internal wikis, spreadsheets, knowledge bases, and other structured or unstructured sources.
Is our company data safe?
Security depends on the architecture. We can design custom LLM systems with access control, approved sources, permissions, private infrastructure options, human review, and monitoring.
Do we need fine-tuning or RAG?
Not always. Many business use cases work best with RAG because the model can retrieve current company knowledge. Fine-tuning is useful when you need specific tone, behavior, classification, or domain patterns.
Can the LLM connect to our existing tools?
Yes. A custom LLM can be connected to Slack, CRM systems, Google Workspace, Notion, internal tools, dashboards, support platforms, APIs, and custom interfaces.
How long does it take to build a custom LLM?
A prototype can often be built in a few weeks. More complex systems with multiple data sources, permissions, integrations, QA, and production deployment take longer depending on scope.
What happens after launch?
We can support monitoring, source updates, model improvements, prompt refinement, new integrations, QA, documentation, and ongoing development as your team starts using the LLM in real workflows.
How do we know what the LLM should answer first?
We start by identifying where your team loses time searching, asking, copying, or repeating information. Then we prioritize the highest-impact knowledge workflows for the first prototype.