AI & Data
AI & Automation for commerce and operations
We implement AI agents, internal copilots and LLM-based automations that move real metrics — conversion, AOV, response times, productivity. We use Claude, OpenAI, open models and Adobe Sensei depending on the business case.
Cases where AI makes a difference
We focus on uses where return is measurable in 60-90 days. We skip the ones that sound great but don't move the needle.
- Conversational shopping assistants with catalog context
- Automated product description generation
- PIM enrichment: tags, categories, attributes
- First-level support with human handoff
- Automatic ticket summarization and categorization
- Real-time personalization (offers, banners, search)
Agents and internal copilots
We automate repetitive work — not to replace your team, but to let them focus on value work. Specific copilots for marketing, merchandising, operations or customer service.
- Internal copilots with RAG over your docs and data
- Agents orchestrating steps across systems (ERP, PIM, Commerce)
- Marketing team assistants (briefs, copy, segmentation)
- Report automation and smart alerts
- Integration with Slack, Teams and internal tools
- Guardrails, logging and observability by design
Stack and models
We're not vendor-loyal. We pick the model per task: Claude for reasoning and code, OpenAI for certain workflows, open models (Llama, Mistral) for specific self-hosted tasks, Adobe Sensei integrated to Experience Cloud.
- Anthropic Claude (Opus, Sonnet, Haiku)
- OpenAI GPT and embedding models
- Self-hosted open models when privacy requires it
- Vector stores (pgvector, Pinecone, Weaviate)
- Orchestration with LangGraph, custom or Bedrock Agents
- Adobe Sensei / Sensei GenAI integrated with Experience Cloud
Responsibility and cost under control
AI without controls is risk. We design evaluations, guardrails and cost monitoring from the first prototype.
- Automated evals before each release
- Prompt caching and token optimization
- Deterministic fallbacks when the model fails
- PII detection and redaction before sending to LLMs
- Cost and latency dashboards per flow
- Usage policies aligned to client compliance
Frequently asked questions
- Which model do you recommend by default?
- For most enterprise cases we start with Claude (Sonnet or Haiku depending on latency/cost) for its reasoning quality and long-context handling. We benchmark against alternatives in the POC per task.
- What if my data is sensitive and I can't send it to a public LLM?
- Options: use zero-data-retention providers (Claude via Anthropic API, OpenAI Enterprise, Bedrock), self-hosted open source models, or architectures where only embeddings or anonymized prompts leave the perimeter. Defined in discovery per data classification.
- How much does a copilot or agent cost to run?
- Varies by volume and model. An internal assistant with 1000 active users can run $500-2000 USD/month in inference costs. With prompt caching, smart routing and smaller models for routine tasks, costs drop 40-70%.
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Learn more →Want to discuss your project?
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