ATIMIS
Our Services
Specialized engineering for scalable systems.
Enterprise AI & RAG Systems
Who this is forEngineering teams deploying LLMs into regulated workflows where accuracy is non-negotiable.
The ChallengeProbabilistic models in precision-driven industries create liability. Standard RAG pipelines lack the auditability required for enterprise regulatory standards.
What We BuildDeterministic retrieval systems with strict citation enforcement. Air-gapped deployment capable. We replace opaque logic with observable reasoning steps.
Why this approach worksWe treat LLMs as untrusted components, wrapping them in rigid validation layers. Reliability comes from the system architecture, ensuring safety by design.
Tech Stack
LlamaIndexLangChainQdrantPostgres (pgvector)vLLMDocker
Internal Tools & Process Automation
Who this is forOperations teams managing critical data workflows and business requirements.
The ChallengeManual data entry and spreadsheet management introduce human error and liability risks. Critical processes lack type safety and access controls.
What We BuildEnforced workflow engines with type-safe state management. Multi-step approval systems with immutable audit trails to prevent invalid states.
Why this approach worksWe encode business rules directly into the software schema. Compliance becomes the path of least resistance, reducing operational risk by design.
Backend, API & Data Platforms
Who this is forScale-ups where initial architectural choices have become growth bottlenecks.
The ChallengeRacing conditions, database locks, and slow queries degrade user trust. Scaling requires architectural precision, not just more resource allocation.
What We BuildHigh-throughput event queues and idempotent API layers. Read-heavy caching strategies ensuring data consistency under load.
Why this approach worksWe design for predictable latency at scale. Your system remains stable and responsive even during unpredictable usage spikes.
Applied Computer Vision
Who this is forIndustrial or logistics sites where manual visual inspection creates margin for error.
The ChallengeCloud APIs often fail on latency and cost for real-time video. Hardware constraints in the field require efficient, local processing.
What We BuildEdge-deployed inference models optimized for limited compute. Robust systems handling variable lighting and occlusion in real-time.
Why this approach worksWe bring intelligence to the data source. Optimization for the edge ensures accuracy without reliance on unstable connectivity.
Tech Stack
OpenCVYOLOv8TensorRTCUDAPythonC++