We Build AI That Answers From Your Data
We build RAG architecture, deploy local LLMs on your own servers, and automate the parts of your workflow that don't need a human. AI that makes money instead of burning tokens.
- Answers grounded in your own knowledge base
- Answers grounded in your own knowledge base
- Local deployment option, your data stays yours
- Local deployment option, your data stays yours
- Free AI readiness check
- Free AI readiness check
Key Challenges We Solve
TWS adapts AI integration to how much your business can actually risk getting wrong.
Startups & Small Teams
You need AI to be a differentiator, not a science project that never ships.
You don't know where to start, and every option looks like a rabbit hole.
An AI Readiness Check gives you three specific, scoped ideas for your actual product.
A wrong AI answer in front of a user is worse than no AI at all.
RAG grounds every answer in your own knowledge base, so the model isn't guessing from the open internet.
You can't afford a team dedicated to prompt engineering.
Prompt and pipeline optimization is built into the engagement, so you're not paying for token waste as a hidden ongoing cost.
Sending customer data to a third-party API makes you nervous.
Local LLM deployment keeps sensitive data on your own infrastructure when that's a requirement.
You need this live before your runway makes the decision for you.
Scoped AI features ship in weeks.
Mid-Sized, Growing Products
Competitors have shipped AI features and the pressure to catch up is real, but a rushed integration creates more support tickets than it solves.
Your support team is drowning in repetitive questions.
A RAG-powered assistant answers from your actual documentation, cutting repetitive tickets without inventing answers.
Early AI experiments hallucinated and damaged trust.
Retrieval-grounded answers replace open-ended generation for anything where accuracy matters.
Token costs are growing faster than the value the AI feature delivers.
Pipeline and prompt optimization gets the same output for meaningfully fewer tokens.
You want AI agents that do something, not just chat.
Autonomous agents call your APIs and complete tasks, like qualifying a lead or triaging a ticket.
Your data governance team is wary of any AI integration.
Local deployment and scoped data access are designed from the start.
Enterprises & Complex Projects
At this scale, an AI rollout that goes wrong is a compliance incident, not just a bad user experience.
Regulatory or contractual terms restrict sending data to external APIs.
Local LLM deployment on your own infrastructure keeps data inside your compliance boundary entirely.
Multiple teams want AI features and there's no consistent architecture.
A single RAG and agent architecture serves multiple use cases instead of every team building its own one-off integration.
Leadership wants to see ROI, not just a demo.
Every engagement is scoped against a specific metric, like hours saved or tickets deflected.
You need audit trails for what the AI actually did.
Agent actions are logged and traceable, which matters when a regulator or an internal audit asks what happened.
Your existing systems weren't built with AI integration in mind.
Custom tools and API integrations connect your legacy systems to the model layer without a rebuild.
What Changes the Day AI Starts Working for You
Three things that are different from a generic chatbot integration.
Retrieval-Augmented Generation
The model answers strictly from your knowledge base, not the open internet, so accuracy isn't a gamble.
Local LLM deployment
Run models on your own servers when data can't leave your infrastructure, full stop.
Prompt and pipeline optimization
You stop paying for tokens that don't add value, without sacrificing answer quality.
From Manual Process to Working System
AI integration done right looks like a real workflow that used to take hours now taking minutes, with answers your team can actually trust.
TeamZone, an AI-driven workplace management platform, is a system TWS built where AI is core to the product, handling real workplace data and decisions in production.
- 53%Faster deployment
- 40%Less time spent on workplace admin
- 30%Reduction in manual tasks
- 25%Higher workplace utilization
Autonomous AI Agents
AI agents call APIs and make decisions on their own.
One example: a lead-generation agent that parses sites, validates contacts, and writes personalized outreach without a human reviewing every step.
We build these on LangChain, with custom tools built specifically around your process, not a generic template.
How AI & LLM Integration Solves Real Problems
Four ways grounded AI changes what your team can actually do.
Support Stops Drowning in Repeat Questions
A RAG-powered assistant retrieves the actual answer from your knowledge base before generating a response, instead of guessing from general training data.
That's what separates an assistant that deflects real tickets from one that creates new ones by getting confident answers wrong.
AI Stops Being a Token-Burning Experiment
Prompt and pipeline optimization cuts the tokens spent per interaction without cutting the quality of the answer.
That turns "AI is expensive and we're not sure it's worth it" into a feature with a cost you can actually defend.
Sensitive Data Never Has to Leave Your Servers
Running the model on your own infrastructure means prompts, documents, and outputs never transit a third-party API when that's a hard requirement.
That's the difference between "we'd love to use AI here" and actually being allowed to.
Agents Do the Work, Not Just Describe It
An agent built on your actual tools can validate a contact, update a record, or triage a ticket, not just draft a suggested reply for a human to send.
That's what turns AI from a productivity nice-to-have into hours actually taken off someone's plate.
What Our Clients Say
I've been very impressed with how collaborative the Team Work Spirit team has been over the past year. They've always engaged as an embedded part of our team, and never as a separate entity with extra barriers to communication.
Chuck GossDirector of Development at KizenOleksandr is a very knowledgeable developer. He has worked well with our team and is good at communicating and helping us solve problems. We look forward to working with him in the future.
Jacob NemetzFounder & CEOThe TWS team did an excellent job in consulting with us on DevOps strategies as well as driving strategy implementation to completion in a very timely manner. The team is very professional.
Jude NwokoCTO, Okada BooksI found Oleksii to be very knowledgeable and reliable. We always know what we would get with him and when we would get it. Oleksii was available to discuss decisions and present options to us.
Tal PerryFounder, LightTagOleksandr and his team helped with our work in a timely manner, provided excellent communication, adherence to deadlines, and went above and beyond the scope of the project.
Marcus ArkanFounder & CEOOleksandr has a great work ethic and very good communication skills. He took some very complex ideas and programmed them out for us with excellent quality. He is very responsible and responsive.
Joel TherienCEOI had the pleasure of working and learning with them. They were dependable and reliable to go above and beyond expectations when needed to get the required work complete.
Melissa CohoeProject ManagerOleksii really knows everything that is required to get the job done. Several times during the contract he was given short notice about problems that we were experiencing and with super speed in each case he responded.
Andrew BlakeFounder & CEO
How We Work
Four steps, from idea to a system your team actually uses.
Scope against a real metric
We define what success looks like, hours saved or tickets deflected, before writing a prompt.
Build the retrieval layer
Your knowledge base gets structured so the model can actually find the right answer.
Optimize the pipeline
Prompts and chains get tuned for accuracy and cost together.
Ship and measure
The feature goes live against the metric it was scoped for, with a way to prove it worked.
Our Tech Stack That Drives Results
- Node.js
- Express
- Nest.js
Begin Your AI Integration Journey
Your workflow, our AI engineers, let's find the real use case.
Get the AI Readiness Check
Fill out a short brief on your product. We'll send back three specific ways to add LLMs or AI agents to your workflow that would move a real metric.
Get my AI readiness check