B2B SaaS
Datar
A B2B platform cross-checking public and private sources to validate policies, fleets and operations for its insurance clients.
- +42%
- Cancellations avoided
- -70%
- Reporting time
- 16
- Integrations
PXL INTELLIGENCE
Most requested
AI applied to real business problems.
We design predictive models, intelligent agents, internal assistants and solutions based on artificial intelligence that improve decisions, produce actionable predictions and unlock measurable competitive advantage.
- 6 to 16 weeks per initiative
- Typical duration
- Team depends on scope
- Team
- PXL Project, PXL Dedicated or PXL Flex
- Model
What's included
Modular, by data maturity and use case
Autonomous agents connected to operations
Internal and external assistants integrated with your documentation, CRMs, ERPs and knowledge bases. Controlled access and traceability by role.
Predictive models and forecasting
We forecast sales, demand, inventory or customer churn with models trained on your historical data. Predictions that beat fixed rules and adjust as your operation changes.
Integration with LLM models
OpenAI, Anthropic, open source models on your own infrastructure, and vertical models. We recommend based on use case, cost and data privacy.
RAG systems (Retrieval Augmented Generation)
We connect AI with documents, PDFs, databases and internal systems for contextual answers with verifiable citations.
Fine-tuned and specialised models
Where the case justifies it, we train models on your proprietary data to reduce hallucination and improve vertical accuracy.
Computer vision and document processing
Advanced OCR and document classification models (invoices, contracts, forms) using ML/NLP. High accuracy, cross-validation and handling of edge cases. The operational pipeline around the model (where the document enters, where it is routed) is covered by PXL Automation.
LLMOps and AI observability
Deployment and versioning of models and prompts, monitoring of accuracy, bias and hallucinations, and control of token and compute costs. Without observability, AI is a black box.
Training and governance
We define the usage policy, access control for sensitive information and training for the team. Badly governed AI is a regulatory and reputational risk.
Customer service agents on Amazon Connect
Voice and chat assistants built with Amazon Connect's agentic CX designer (Amazon CX Designer), connected to your systems. With Live Sync they guide users inside your app or website during the call.
Conversational and generative interfaces
Interfaces built in real time from what the user asks in natural language, instead of fixed menus and forms. Connected to your data, with controls so each role sees only what it should.
Who it's for
Is PXL Intelligence what you need?
We'll help you decide quickly. If we are not the right match, we point you to who is.
This is not for you if…
- You want to integrate AI only to announce it in a press release
- You expect AI to replace your entire team in 3 months
- You have neither clean data nor documented processes (start with PXL Discovery)
- You need a basic FAQ chatbot (an off-the-shelf SaaS costs you less)
- There is no tolerance for iteration: AI requires measured experimentation
This is for you if…
- You have historical data that can train predictions more accurate than fixed rules or manual thresholds
- You handle a high volume of documents, tickets, emails or queries
- Your sales or support team needs instant information from scattered sources
- You want to offer AI as a differentiating feature of your own product
- You have valuable proprietary data that can feed specialised models
Problem
What badly applied AI costs the business.
Many companies are integrating AI purely as a marketing trend. The result is models disconnected from the business, agents that hallucinate and products that generate more noise than value. Badly implemented AI does not merely fail to solve problems, it multiplies them: wrong answers reaching customers, automation that breaks critical processes, and projects that die 6 months after the pilot.
Models answering with incorrect or out-of-date information (hallucinations)
Agents that do not understand context and produce useless generic answers
Risks of exposing sensitive data through poor RAG architecture
Uncontrolled token costs with no observability and no limits
Brilliant pilots in demos that never reach production
Teams abandoning the tool because it was never integrated into their real workflow
Outcomes
AI that multiplies capability and sharpens decisions.
The difference is not using AI, it is using it where human judgement arrives late or goes astray. AI implemented well shows up in concrete business outcomes: sales closing with instant information, predictions that beat fixed rules, agents answering with verifiable context, and teams that stop improvising decisions.
A measurable reduction in human error on decisions that used to depend on intuition or individual experience
Faster answers to internal and external customers with verifiable context
Expert judgement scaled across the team (the model replicates what only the most senior person knew how to do)
Real competitive advantage over competitors who are barely implementing FAQs
Proprietary data turned into strategic company assets
Decisions taken on information synthesised in seconds, not hours
Process
The 5 steps of a typical engagement.
Diagnosis of AI opportunities
We identify cases where AI creates real advantage (not merely where it automates): decisions that today depend on a senior expert, high-volume content that requires judgement, or predictions that beat fixed rules. We prioritise 1 to 3 initiatives with measurable impact.
AI and data architecture
Design of the AI stack, data sources, embeddings, vector store, the models to use and the RAG architecture. Definition of operational rules and observability.
Prototype and validation
Building a functional prototype with real data. Evaluation of accuracy, latency, cost and experience with pilot users.
Productisation
Integration into internal systems, access controls, continuous monitoring, token observability and a continuous improvement plan.
Adoption and governance
Training for the team, a usage policy, success metrics and an evolution plan. Iteration based on real usage, not assumptions.
Related case studies
Projects where this service played the leading role.
B2B SaaS
Internal RAG agent for 800+ employees
A RAG agent for 800+ employees with access to corporate documentation, procedures and operational data. Traceability and role control.
- 800+
- Users
- -65%
- Internal support tickets
- 12s
- Average response
B2B SaaS
Document extraction and classification with AI
An AI model for extracting and classifying shopping-centre operational documents (contracts, reports, invoicing). A vector database with semantic search, cross-validation and handover to a human in exceptional cases.
- 99%
- Accuracy
- -80%
- Processing time per document
Stack | Tools | Standards
The technologies we use on PXL Intelligence.
AI with proven models, frameworks and observability.
- OpenAI
- Anthropic
- Llama
- Mistral
- LangChain
- LlamaIndex
- Semantic Kernel
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Python
- FastAPI
- Node.js
- PostgreSQL
- MongoDB
- AWS Bedrock
- Azure OpenAI
- GCP Vertex AI
- LangSmith
- Helicone
- Langfuse
- Tesseract
- Azure AI Document Intelligence
- AWS Textract
- TensorFlow
- OpenClaw
- Hugging Face
- Google Gemini
- Ollama
- Whisper
- Vercel AI SDK
- Google Customer Engagement Suite
- AutoGen
- Gemma
Frequently asked questions
What almost everyone asks.
Does AI replace entire teams?
AI amplifies human judgement and automates decisions that previously required an expert.
Can you connect the AI to our internal systems?
Yes, we build agents and assistants with RAG connected to your internal systems and data, not isolated demos.
Does Pixzelle build on Amazon Connect?
Yes. We design and build customer service agents on Amazon Connect with its agentic CX designer (Amazon CX Designer) and connect them to your systems, mobile app and website, including Live Sync experiences that guide users inside the app during the conversation.
Which models do you use?
We work with different providers depending on the case: OpenAI, Anthropic, and open-source models, among others. We choose the model based on each project's privacy, cost, and performance needs.
How do you handle data privacy?
Your data and your customers' data isn't used to train third-party models. When we connect AI to your systems, we define from the design stage what information goes out, where, and under what controls, so both you and your end users are protected.
What does it cost to run AI in production?
The cost depends on usage volume, model complexity, and whether we use an external provider or our own infrastructure. Those are the variables that determine price in each case.
How soon do I see results?
It depends on the use case: a well-scoped internal assistant can show measurable results within weeks, while a more complex decision-making system takes more time to tune.
Related services
Landing AI in production demands three solid foundations
PXL DATA
PXL Data
Data platforms. ETLs, data warehouses, analytics and governed dashboards for deciding with evidence.
PXL CONNECT
PXL Connect
Integrations & IoT. APIs, legacy systems, hardware and synchronisation of the digital and physical ecosystem.
PXL AUTOMATION
PXL Automation
Operational orchestration. The operational flow that executes: when the input arrives, the model decides and the decision is routed into the business systems. Without PXL Automation, the model is an isolated service with no real use.
AI in production, not in a demo.
A 30-minute diagnosis of AI opportunities in your operation.