Agents & harness
Custom agent engineering
- Agents with tools, memory and secure access to your systems
- Multi-agent orchestration and context engineering
- Evals, guardrails and observability to ship them to production
Reliable AI, not just demos
Artificial intelligence
Ready to power up
your business with
artificial intelligence?
We build assistants, automations and analytics with generative AI, integrated into your operation. From the first use case to full rollout.
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Artificial intelligence applied to business
We design and ship AI solutions that plug into your existing systems: automating processes, improving decision-making, and generating real business impact.
Operations
Intelligent automation
- Automate repetitive tasks and internal processes
- Operational workflows integrated with your ERP
- Fewer manual errors and shorter cycles
Frictionless ops
Sales
AI-driven pipeline
- Smart lead and opportunity scoring
- Automated follow-ups and proposal generation
- Actionable customer-behavior insights
More conversions, less time
Customer service
24/7 assistants
- Assistant trained on your company's knowledge
- Automated, personalized, contextual replies
- Integrated with your support and internal systems
Support that scales
Technology & IT
Productivity for tech teams
- AI baked into your web & mobile apps
- Automated testing and technical workflows
- Smarter use of IT resources
10× engineering teams
Finance
Predictive analytics
- Predictive financial analysis
- Automated control reports
- Early detection of anomalies and patterns
Decisions on real-time data
Legal
Document intelligence
- Automatic extraction of key information
- Assistance in document analysis and drafting
- Comparison and validation of regulatory content
Hours back to your team
Harness engineering
From prototype to a reliable AI system
A good demo isn't a system. We build the harness: the engineering around the model —context, tools, orchestration, evaluation and observability— that makes AI accurate, safe and sustainable in production.
1
Context
Context engineering: what the model sees, when and in what shape.
2
Tools
Secure access to your systems, APIs and data, with scoped permissions.
3
Orchestration
Agent loops and multi-agent flows for complex tasks.
4
Evals & guardrails
Measurable quality, automated tests and limits against errors.
5
Production
Observability, cost control and continuous improvement on real data.
Got a use case where AI has to be genuinely reliable?
Let's talkSuccess stories that drive impact
Mid-market and enterprise companies already running with AI.
Predictive maintenance: machines warn before they break
Challenge
Unplanned line stops cost USD 8,000 per event, averaging 6 events a month.
Solution
Predictive model over sensor telemetry that anticipates failures and schedules maintenance in idle windows.
−70%
unplanned stops
USD 35K
monthly savings
+22%
OEE
An assistant that solves two out of three queries
Challenge
70% of incoming queries were FAQs that saturated the human team.
Solution
Assistant trained on the company's own knowledge base, integrated with WhatsApp and the customer portal, with automatic escalation.
65%
resolved without a human
+12 pts
NPS post launch
−30%
average response time
200 hours/month of reconciliation collapsed into hours
Challenge
Admin team spent 200 hours each month matching invoices, delivery notes and bank movements.
Solution
RPA + GenAI extraction pipeline that matches documents automatically and surfaces only ambiguous cases for review.
95%
automated
8 hrs
human work left / month
0
errors in last quarter close
Delivery route-planning agent
Challenge
Every morning, building the delivery plan was done by hand: deciding which orders go in each vehicle, in what order and along which route took hours and depended on one person's experience. When orders spiked or a driver called in sick, redoing the whole plan was slow and costly.
Solution
We built an AI agent that takes the day's orders and the real constraints — each vehicle's capacity, time windows, zones and priorities — and produces the delivery plan: which order goes in which vehicle and the optimal stop sequence per route. The team reviews and tweaks instead of planning from scratch, and can recompute in minutes when anything changes.
Minutes
to build the plan (was hours)
−18%
kilometers per route
+ deliveries
per vehicle, same fleet
Bank and card reconciliation, automated with AI
Challenge
The finance team spent days matching bank statements and credit-card settlements against sales and collections — thousands of movements with differences that were hard to trace by hand.
Solution
An AI reconciliation engine that automatically matches bank movements and card settlements against sales and collections, explains every difference and leaves only the exceptions for human review.
Auto
bank + card reconciliation
Days → hrs
financial close
Exceptions
all a human touches
Ready to start your project?
Let’s discuss how ACLN can help transform your operations with Odoo, BI, RPA, and AI.
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