A pediatrician who answers at 3 a.m.
An AI agent offers guidance on pediatric questions from a clinical knowledge base, at any hour. It was later moved to a platform that measures its performance.
24/7available for guidance, at any time of day
Services / AI & Data Science
Service · AI & Data ScienceAgents that do the heavy lifting. In your channels.
We design, build, and run AI agents that read documents, cross-check numbers, handle conversations on WhatsApp or Teams, and explain what they find. With the model best suited to each task, and safety controls built into the code.
10 sec
to evaluate each agent conversation, down from 15 to 20 minutes. Mobility, Mexico.
The problem
The situation today.
AI pilots stay stuck in testing and never reach production.
Generic assistants don't know your business and don't connect to your systems.
No one can prove that the agent gets it right, or that it's safe.
Capabilities
What we do well.
Agents that do the heavy lifting
They do in minutes what takes a person hours: reading, comparing, calculating, and summarizing.
- Read documents (OCR) and cross-check them line by line
- Recalculate numbers and flag what doesn't add up
- Analyze, summarize, and write for a human reader
Agents connected to your operations
A team of agents that works in your channels and with your systems, coordinated by a central agent.
- Channels: WhatsApp, Teams, Slack, web, and voice
- A central agent that coordinates specialist agents
- Connection to your systems (MCP), memory, and a person to approve when needed
The best model for each task
We don't tie your operations to one vendor: we choose the model that best handles each task, based on quality, speed, and cost.
- Claude, GPT, Gemini, Llama, Mistral, and Nova
- On Bedrock, Vertex AI, or Azure OpenAI
- Comparison and migration, measured with your own evaluator
Your knowledge, searchable
Your manuals, standards, and documents become answers, each with its source.
- Search across standards, manuals, and technical knowledge bases
- Search by meaning, not just by keywords (vector databases)
- Answers with their source
Data science in production
Models that anticipate risk or demand, put to work in your operations and not just in a test.
- Risk and forecasting models
- From notebooks to automated processes with a model registry (MLOps)
- GPU-optimized inference
AI you can verify
Before every release, we use real cases to measure whether the agent gets it right and is safe.
- Evaluation with test cases before every release
- Adversarial and security testing
- Safeguards in the code, not just in the instructions
How it works
A complete agentic architecture, seen from the inside.
Two walkthroughs: agents that do the heavy lifting, like evaluating every conversation of a customer service team, and agents that serve people in your channels, with your systems behind them.
Models
The best model for each task.
We work with models from Anthropic, OpenAI, and Google, open-source models, and each cloud's native models. We choose by quality, speed, and cost, task by task.
Systems we've built
What we built and what it achieved.
Real cases, described by industry. Client names are kept confidential.
A pharmacist on WhatsApp, with safety rules built into the system.
It answers from a collection of 1,078 medical documents and refers anything clinical to a specialist. Before it was opened to patients, it had to pass a test of 63 clinical cases.
100%safe answers in a test of 63 clinical cases, before opening it to patients
The quality of every conversation its agent handles, evaluated in seconds.
Before, a person manually reviewed a sample of 30 conversations. Now an AI evaluator scores 300 on five quality criteria, using the same yardstick for all of them.
10 secto evaluate a conversation, down from 15 to 20 minutes by hand
Four credit risk models, from Excel to running in the cloud.
The models lived in local working files. Today they're trained by an automated process, every version is recorded, and all four are available as cloud services.
< 5 minto get a new model up and running
An agronomist who answers at any hour.
An AI agent answers its users' technical questions about soil and finds information by meaning, not just by exact words. Its team also has a dedicated environment to build new models and put them to work.
2 wksfrom the idea to having it up and running
An agent that tests its app the way a user would.
An AI agent walks through the app step by step, just like a user, and reports every flow that breaks.
15 minto test the app, down from 2 to 3 hours
The AI that reads its contracts, chosen through tests on its own documents.
Five AI models that read and interpret contracts, including scanned ones, were compared. Everything was measured against the client's real documents before making a choice.
1,350measured tests to compare the five models
Cybersecurity risk, explained for whoever is asking.
An AI agent explains the risk rating and presents the same finding differently to leadership and to the technical team.
Explainableto leadership and to the technical team
Its audio files, processed with AI much faster on the same machine.
We reorganized how its audio models ran so the same machine could handle two jobs at once. It was measured with 10, 50, and 1,000 audio files, without a single out-of-memory failure.
42%faster with 1,000 audio files, compared with how it ran before on the same machine
Technologies
With the tools you already use.
Models
ClaudeGPTGeminiLlamaMistralDeepSeekNova
AI platforms
BedrockAgentCoreVertex AIAzure OpenAISageMakerHugging Face
Build
StrandsLangGraphMCPPython
Next step
Start with an assessment.
Within weeks, you know where the value is. From month one, you see it.
Book your assessment Explore our services in more detail →