Automate

AI Automation

Systems that think, shipped to production.

Most "AI projects" stall at the demo. The gap is never the model — it is everything around it: retrieval that returns the right passage, evaluation you can trust, guardrails, cost control, observability, and a fallback for the day the provider has an outage. That part is ordinary software engineering, done carefully.

We build LLM-backed features, retrieval over private data, and agents that carry out real multi-step work against real systems. We are opinionated about evaluation: if a prompt change cannot be measured against a fixed set of cases, it is not an improvement, it is a guess.

This is not something we read about. Shard Soft runs a national-scale project portfolio with a deliberately small team, and it does so because our own operations are AI-native — content, ingestion from Telegram and WhatsApp groups, moderation, translation, and engineering itself. The systems we would build for you are the systems we run.

What this covers

LLM features — assistants, summarisation, extraction, classification
Retrieval-augmented generation over private, multilingual data
Agents that execute multi-step work against real APIs
Evaluation harnesses, regression suites and prompt version control
Arabic-language AI, where dialect and tokenisation actually matter

Typical stack

Claude · OpenAI · Python · TypeScript · pgvector · n8n

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Everything else we do

Sales Automation CRM implementation, lead capture and routing, enrichment, and the follow-up that otherwise depends on someone remembering. Marketing Automation Campaign infrastructure, content pipelines, messaging channels and the attribution to tell what actually worked. Software Development Web platforms, APIs and backends for businesses and institutions — designed for real traffic, real data and long maintenance.