AI Solutions for Business
From a single AI chatbot to a full multi-agent automation platform — we design, build, and deploy AI systems that create measurable business impact.
AI Services We Provide
We cover the full AI implementation stack — from model selection to production deployment.
AI Chatbots
Knowledge-base chatbots powered by RAG — answer customer questions from your documents, 24/7, on any channel.
AI Agents
Autonomous agents that use tools, take actions, and complete multi-step tasks: lead qualification, support, research, content generation.
AI-Powered Features
Add AI capabilities to your existing product: smart search, content generation, recommendation engine, or sentiment analysis.
Document Intelligence
Extract structured data from PDFs, contracts, and invoices. Automate document classification, review, and processing at scale.
LLM Integration & Fine-tuning
Connect GPT-4o, Claude, or Gemini to your product via API. Fine-tune models on your data for specialized domain tasks.
AI Strategy Consulting
Identify the highest-ROI AI use cases in your business, evaluate build vs buy, and create a practical implementation roadmap.
AI Technology Stack
How We Approach AI Projects
Start with the problem, not the technology — model selected to fit your use case
Prototype first — you test before we build the production system
Your data stays on your infrastructure — no LLM data sharing without consent
Cost estimation included — we model token costs before you commit
Human-in-the-loop design for high-stakes decisions
Full observability — every LLM call logged, traced, and auditable
Incremental delivery — each capability shipped and tested independently
Handoff-ready — your team can extend the system after delivery
AI Projects We Delivered
AI systems generating measurable ROI in production.
Document intelligence for a microfinance company — loan processing 45 min to 4 min
A microfinance company was manually reviewing loan applications with 12 attached documents each: income certificates, IDs, employment letters, bank statements. A loan officer took 45 minutes per application.
Document intelligence pipeline: GPT-4o extracts structured data from all document types, cross-validates fields, flags discrepancies, and generates a decision summary. Loan officers review the summary instead of raw documents.
Application review time: 45 minutes → 4 minutes. Loan officer capacity: 8 applications/day → 70 applications/day. Document extraction accuracy: 97.3%.
AI support system for a B2B SaaS — 73% queries auto-resolved
A B2B SaaS company's support team was handling 1,200 tickets per month. 80% were questions that could be answered from the product documentation — but finding the right article took users too long.
RAG-based support bot deployed on the product's help widget: searches across 400 documentation articles, generates specific answers with source citations, and escalates complex issues with full context to the support team.
73% of support queries auto-resolved without human involvement. Support ticket volume: 1,200 → 324 per month. First-response time: from 6 hours to under 10 seconds. CSAT up 24 points.
AI recommendation engine for an online retailer — cart abandonment down 42%
An online retailer's cart abandonment rate was 71%. Analysis showed that 60% of abandoners viewed only one product category — they weren't finding complementary items that would complete their purchase intent.
Recommendation engine using embeddings: real-time "you might also like" based on viewing history, purchase patterns, and product similarity. Integrated into product pages, cart, and post-browse email sequences.
Cart abandonment: 71% → 41%. Average order value up 28%. Recommendation-attributed revenue: 18% of total. Email sequence using recommendations converted at 3.2% vs 0.8% for generic emails.
Pricing
Scoped per project. These ranges reflect the most common AI engagements.
AI Feature / Chatbot
2–4 weeks
- ✓Single AI feature or FAQ chatbot
- ✓RAG on your knowledge base
- ✓One channel (web, Telegram, etc.)
- ✓Admin panel for content management
- ✓1 month support included
AI Agent / System
4–10 weeks
- ✓Autonomous agent with tool use
- ✓CRM / database integration
- ✓Multi-channel deployment
- ✓Analytics & cost monitoring
- ✓3 months support included
Multi-Agent Platform
8–16 weeks
- ✓Multiple agents collaborating
- ✓Custom orchestration layer
- ✓Full observability stack
- ✓LLM cost optimization
- ✓6 months support included
How We Implement AI
Use Case Discovery
We identify the specific task to automate, define success metrics, and select the right model and architecture.
1–3 daysPrototype
A working proof of concept on your real data — tested against your quality criteria before full development.
3–7 daysProduction Build
Full system with error handling, logging, cost controls, and integrations — built for reliability, not just demos.
2–12 weeksMonitor & Improve
Post-launch review of LLM outputs, cost tracking, and iterative improvement based on real usage data.
OngoingWhat Our AI Clients Say
“Loan processing went from 45 minutes to 4 minutes. Our officers can now handle 70 applications a day instead of 8. The document extraction accuracy is 97%, which is better than manual review.”
“73% of support tickets resolved automatically. We went from 1,200 tickets a month to 324. First response time went from 6 hours to 10 seconds. CSAT improved by 24 points. This changed our business.”
“Cart abandonment dropped from 71% to 41%. The recommendation engine now accounts for 18% of total revenue. Something we thought would take a year to build was live in 6 weeks.”
Specific AI Services
Frequently Asked Questions
Which AI model should we use for our project?
Model selection depends on your task type, latency requirements, cost budget, and data privacy constraints. GPT-4o excels at general reasoning and tool use. Claude 4 excels at long-document processing and following complex instructions. Gemini 2.5 is strong for multimodal tasks. We evaluate and recommend the most cost-effective model for your use case.
Will our data be used to train OpenAI or Anthropic models?
No. Enterprise API agreements with OpenAI and Anthropic explicitly state that API data is not used for model training. For maximum privacy, we can also deploy open-source models (Llama, Mistral) on your own infrastructure.
How much does AI integration cost?
An AI feature or FAQ chatbot starts at $1,000. An AI agent with tool use and CRM integration starts at $5,000. A multi-agent platform starts at $15,000. In addition to development cost, there are ongoing LLM API costs — we model these before you commit.
How long does it take to implement an AI solution?
A simple AI feature or chatbot takes 2–4 weeks. An AI agent takes 4–10 weeks. A multi-agent platform takes 8–16 weeks. We prototype first (3–7 days) so you can validate the approach before committing to full development.
Can AI be added to our existing product?
Yes. We integrate AI as a feature layer on top of your existing application via API. Common additions: smart search, content generation, document analysis, recommendation engine, or a conversational interface — without rebuilding your product.
Let's Find Your Best AI Use Case
A 30-minute discovery call is enough to identify where AI creates the most value in your business.