Automate the Repetitive. Amplify the Human. — Practical AI Solutions That Actually Work
AI isn't magic — it's a tool. The companies winning with AI aren't the ones with the fanciest models. They're the ones who identified the right problems to automate, built focused solutions, and measured the impact in hours saved and revenue generated. At Desisle, we help businesses cut through the AI hype and find practical applications that save time, reduce errors, and scale operations — without requiring a data science team or a PhD in machine learning.
The Pain Points That Brought You Here
Your support team answers the same 20 questions 100 times a day.
"How do I reset my password?" "What's your pricing?" "How do I cancel?" That's 2,000+ hours per year wasted on questions a chatbot could handle in seconds. Your human agents should handle complex issues, not FAQ responses.
Your team copies data between tools manually.
CRM → spreadsheet → email → invoice → project management tool. Every manual step is an error waiting to happen and 30 minutes of human time that could be 30 seconds of automated workflow.
You have data everywhere but insights nowhere.
Dashboards show numbers. Nobody can explain what they mean or what to do about them. Your BI tool has 50 charts that nobody looks at. You need AI that surfaces patterns, anomalies, and recommendations — not just more charts.
You're processing documents by hand.
Invoices, contracts, applications, receipts, resumes — all manually read, data extracted, entered into systems. Hundreds of hours per month of mind-numbing work that's both slow and error-prone.
You're hearing "AI" in every meeting
and don't know where to start. Everyone wants AI features in the product. The CEO read an article about ChatGPT. The board wants an AI strategy. But nobody has defined what "AI" means for your specific business or which use cases have real ROI.
Your competitors are shipping AI features
and you're falling behind. They have smart search, personalized recommendations, automated reports, and natural language interfaces. You have... a search bar with keyword matching.
What We Build
AI Chatbots & Virtual Agents
Custom chatbots for customer support, lead qualification, and internal helpdesks. Not basic rule-based bots — AI-powered assistants trained on your product documentation, knowledge base, and conversation history.
- Customer support bots: Handle 60-80% of support queries automatically. Escalate complex issues to humans with full context. Deployed on website, Slack, WhatsApp, or in-product.
- Sales qualification bots: Engage website visitors, qualify leads using your ICP criteria, and schedule meetings with your sales team — 24/7, no coffee breaks.
- Internal helpdesks: IT support bots for employees, HR policy bots, onboarding bots that answer new-hire questions about benefits and processes.
- Powered by: OpenAI GPT-4, Claude, or open-source models (Llama, Mistral) depending on your privacy and cost requirements. RAG architecture for knowledge-grounded responses.
Workflow Automation
Connecting your tools with automated workflows that eliminate manual data transfer, reduce errors, and save hours per week.
- Tool-to-tool integration: CRM ↔ email ↔ Slack ↔ spreadsheet ↔ invoice ↔ project management
- Trigger-based automation: "When a new lead enters HubSpot → enrich with Apollo data → score → notify sales in Slack → create task in Asana"
- Multi-step workflows: Complex automation chains with conditional logic, error handling, and notifications
- Built on: Zapier, Make (Integromat), n8n (self-hosted for privacy), or custom Node.js integrations for complex logic
AI-Powered Analytics & Dashboards
Dashboards that don't just show data — they explain it, predict trends, flag anomalies, and recommend actions.
- Anomaly detection: Automatically flag unusual patterns in your metrics (spike in churn, drop in activation, unusual spending)
- Predictive analytics: Forecast revenue, churn, demand, and capacity using historical data and ML models
- Natural language queries: Ask your data questions in plain English — "What caused the conversion drop last week?" — and get answers
- Automated reports: Weekly/monthly reports generated and sent automatically with key insights highlighted
Intelligent Document Processing (IDP)
Automated extraction and processing of data from unstructured documents using OCR, NLP, and machine learning.
- Invoice processing: Automatically extract vendor, amount, date, line items, tax → push to accounting software
- Contract analysis: Extract key terms, dates, obligations, and flag deviations from standard templates
- Resume screening: Extract skills, experience, education → rank against job requirements
- Form processing: Application forms, surveys, registrations → structured database records
- Accuracy: 95-99% extraction accuracy with human review for edge cases
AI Feature Integration
Adding AI-powered features into your existing SaaS product to increase user value, differentiation, and stickiness.
- Smart search: Natural language search that understands intent, not just keywords
- Recommendation engines: "Users who did X also found Y useful" — personalized suggestions
- Automated classification: Categorizing support tickets, documents, transactions, or content automatically
- Summarization: Automatically summarizing long documents, meeting transcripts, or data reports
- Natural language interfaces: Let users interact with your product using plain English instead of complex filters and forms
Our Process
Phase 1 — Discovery & Use Case Identification (Week 1)
Before building anything, we identify WHERE AI will have the highest ROI in your business.
- Audit current workflows for automation opportunities
- Identify repetitive tasks consuming the most human hours
- Evaluate data availability and quality (AI is only as good as its data)
- Prioritize use cases by impact × feasibility
- Define success metrics for each use case
Phase 2 — Design & Architecture (Week 2)
Design the solution architecture, select the right AI models and tools, and plan the integration.
- Model selection (GPT-4, Claude, open-source, custom ML)
- Data pipeline design (where data comes from, how it flows, where results go)
- Integration architecture (APIs, webhooks, database connections)
- UI/UX design for AI features (chatbot interface, dashboard, notifications)
- Privacy and security planning (data handling, PII, model access controls)
Phase 3 — Build & Train (Weeks 3-6)
Develop the solution, train models on your data, integrate with your systems.
- Chatbot: knowledge base ingestion, conversation flow design, testing with real queries
- Automation: workflow building, testing with real data, error handling
- Analytics: model training, dashboard creation, alert configuration
- Document processing: OCR/NLP pipeline, extraction rules, accuracy testing
Phase 4 — Deploy & Optimize (Weeks 6-8)
Launch, monitor, and improve based on real-world performance.
- Staged rollout (internal → beta users → full deployment)
- Performance monitoring (accuracy, response time, error rates)
- User feedback collection
- Model fine-tuning based on real interactions
- Documentation and team training
Pricing
| Solution | Timeline | Starting At |
|---|---|---|
| AI Chatbot MVP | 4-6 weeks | $3,000-$8,000 |
| Workflow Automation (5-10 workflows) | 2-4 weeks | $2,000-$5,000 |
| AI Analytics Dashboard | 4-8 weeks | $5,000-$15,000 |
| Document Processing Pipeline | 4-6 weeks | $5,000-$12,000 |
| AI Feature Integration (in existing product) | 6-12 weeks | $8,000-$20,000 |
| AI Strategy & Use Case Assessment | 1-2 weeks | $2,000-$4,000 |
| Monthly AI Optimization Retainer | Ongoing | $2,000-$5,000/month |
Results
-
SaaS Support AI support bot rollout72% of queries handled automatically Response time dropped from 4 hours to 8 seconds Team focused on higher-complexity issues
-
E-commerce Ops Cross-tool workflow automation35 hours per week of manual work eliminated Shopify, shipping, CRM, and accounting synced Zero transfer errors since launch
-
FinTech Analytics Anomaly detection systemFraud ring flagged 3 weeks earlier than manual review Estimated loss prevention of $340K
-
Legal Firm Contract document processing500+ contracts processed per month Review time cut from 2 hours to 10 minutes 97.5% extraction accuracy
-
HR Department Resume screening automation200 applications processed per day Recruiter review time reduced by 75% Time-to-hire cut from 6 weeks to 3
SaaS Support: AI chatbot handles 72% of support queries without human intervention → support team capacity freed up for complex issues → average response time: 8 seconds (was 4 hours)
E-commerce Operations: Workflow automation connected Shopify → inventory → shipping → CRM → accounting → eliminated 35 hours/week of manual data entry → zero data transfer errors since launch
FinTech Analytics: AI anomaly detection flagged unusual transaction patterns → caught a fraud ring 3 weeks before it would have been discovered manually → saved estimated $340K in losses
Legal Firm: Document processing pipeline extracts key terms from 500+ contracts per month → review time reduced from 2 hours per contract to 10 minutes → accuracy: 97.5%
HR Department: Resume screening AI processes 200 applications/day → ranks candidates by fit score → recruiter review time reduced by 75% → time-to-hire dropped from 6 weeks to 3
Who This Is For
SaaS companies wanting to add AI features
to their product (smart search, recommendations, NLP)
Support teams drowning in repetitive queries
that a chatbot could handle
Operations teams
with manual data transfer between tools that should be automated
Companies processing high volumes of documents
(invoices, contracts, applications)
Business leaders wanting AI strategy
— what's hype vs. what's practical for YOUR business
Teams that tried "AI tools" and failed
because the off-the-shelf solution didn't fit their use case
FAQs
Both. For most use cases, fine-tuned LLMs (GPT-4, Claude) with RAG (Retrieval-Augmented Generation) are the fastest and most cost-effective. For specialized needs (anomaly detection, classification, prediction), we build custom ML models. We always recommend the approach that delivers the best ROI.
Chatbot MVP: 4-6 weeks. Workflow automation: 2-4 weeks. Custom AI features: 6-12 weeks. AI analytics dashboard: 4-8 weeks.
No. The best AI implementations augment humans, not replace them. Your support team handles complex issues. Your analysts interpret AI-surfaced insights. Your salespeople close leads the chatbot qualified. AI handles the repetitive; humans handle the nuanced.
We take data privacy seriously. Options include: self-hosted models (no data leaves your servers), enterprise API agreements with OpenAI/Anthropic (data not used for training), PII redaction before processing, and full GDPR/HIPAA compliance.
It depends. AI requires structured, accessible data. We assess your data quality during the discovery phase and recommend data cleanup or enrichment steps if needed. Sometimes the best "AI project" starts with a data cleanup project.
Typically 3-10x return on investment within the first year. A $5,000 chatbot that handles 70% of support queries saves $50,000+/year in support costs. A $3,000 automation project that saves 20 hours/week saves $40,000+/year in labor.
Ready to Put AI to Work (For Real)?
Stop talking about AI. Start deploying it. Practical solutions that save time, reduce errors, and scale your operations.