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
- Output accuracy testing
- Edge case and exception handling
- Permission and access checks
- Workflow reliability testing
- Prompt, logic, and integration improvements
Launch & Handover
- Production deployment
- Team training and usage documentation
- Monitoring and performance review
- Post-launch improvements
- Ongoing development retainer 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 build the AI agent that handles reports, follow-ups, task updates, and internal workflows for you.
Got repetitive work your team should not be doing manually?
Industries We Build For
We build AI agents for teams that need faster reporting, cleaner workflows, and less repetitive manual work.
Financial Services
AI agents for reporting, compliance workflows, client updates, internal task routing, document summaries, and operational processes where accuracy and permissions matter.
SaaS & Software
AI agents for onboarding, product support, customer success workflows, sales follow-ups, churn signals, internal documentation, and recurring team operations.
Real Estate & PropTech
Automation for lead qualification, listing updates, broker follow-ups, client communication, property data workflows, and internal reporting.
Rentals & Property Management
AI agents for booking operations, availability checks, client follow-ups, recurring reports, maintenance workflows, and team coordination across multiple properties or locations.
Crypto & Web3
AI agents for community operations, user support, internal reporting, documentation workflows, lead tracking, and product operations across fast-moving teams.
E-commerce & DTC
Automation for customer support, order workflows, product data updates, campaign reporting, inventory checks, CRM follow-ups, and retention operations.
Healthcare & MedTech
AI agents for internal admin workflows, patient communication support, reporting, documentation summaries, and structured operational processes with careful review logic.
Professional Services
AI agents for agencies, consultancies, legal teams, accounting firms, and service businesses that need better reporting, follow-ups, task management, and internal workflow automation.
Education & EdTech
Automation for student communication, admissions workflows, internal support, course operations, reporting, and knowledge base assistance.
Why Now
Every week without automation means your team keeps spending time on work that could already be handled by an AI agent.
Repetitive work compounds quietly
Manual reporting, follow-ups, data entry, and task updates may look small individually, but together they take hours from the people who should be solving higher-value problems.
Your competitors are already using AI operationally
AI is moving from experimentation to execution. The teams getting value are not just using AI tools manually - they are embedding AI agents into daily workflows.
Your team needs systems, not more tabs
When work lives across too many tools, people spend more time checking, copying, and updating than deciding. AI agents help connect the workflow instead of adding another disconnected platform.
Slow follow-ups cost opportunities
Leads, clients, internal approvals, and team requests all lose momentum when nobody owns the next step. AI agents keep track of what needs to happen next.
Automation gets harder the longer you wait
The more manual processes grow, the harder they become to untangle. Building AI workflows early gives your team cleaner systems before operational complexity turns into a bottleneck.
Tell us what you need -
we'll take it from there
No commitment required to start. Tell us what your team repeats every week, which tools you use, and where work slows down. We’ll identify what can be automated, what should stay human-led, and what kind of AI agent makes sense for your business.
Book a Strategy Call
Frequently Asked Questions
Questions, Answered
What kind of AI agents do you build?
We build custom AI agents for reporting, follow-ups, CRM updates, task management, internal operations, support workflows, knowledge bases, sales processes, and business process automation.
Can an AI agent work with our existing tools?
Yes. We can connect AI agents to tools like CRM systems, Slack, Google Workspace, ClickUp, Notion, Airtable, dashboards, internal platforms, and third-party APIs.
Do you build chatbots or real workflow agents?
We can build both, but the focus is usually on workflow agents. That means the agent does not just answer questions — it can check information, prepare outputs, update tools, trigger next steps, and support business processes.
How long does it take to build an AI agent?
A simple AI agent prototype can often be built in 1–2 weeks. More complex agents with integrations, permissions, data workflows, and testing usually take several weeks depending on scope.
How much does AI agent development cost?
Cost depends on the workflow complexity, number of integrations, data sources, security requirements, and whether you need a prototype, production agent, or ongoing development support.
Do you use AI safely in business workflows?
Yes. We design AI agents with clear rules, human review points, permissions, fallback logic, testing, and monitoring, especially when the workflow involves clients, sensitive data, or important business decisions.
Can you automate reporting with AI agents?
Yes. We build AI reporting agents that collect data, summarize performance, highlight changes, prepare recurring updates, and send reports to the right people or channels.
What happens after the AI agent launches?
We can provide monitoring, improvements, workflow adjustments, new integrations, documentation updates, and ongoing support as your team starts using the agent in real operations.
How do we know which AI agent we need first?
We start by reviewing your repetitive tasks, tools, workflows, and bottlenecks. Then we identify the highest-impact use case - the one that can save time, reduce manual work, or improve visibility fastest.