We build AI that ships to production
RAG systems, AI agents, and LLM integrations engineered for real workflows. We work with CTOs and engineering teams at scale-ups and mid-market companies to turn AI from a prototype into a production asset.
End-to-end AI delivery
From scoping to production deployment — one team, clear ownership, no handoff gaps.
Process-first discovery
We map your workflows before writing a line of code. Use-case selection backed by ROI data from live client projects.
Custom solutions, continuous support
Production-hardened integrations with your existing stack, with ongoing monitoring and iteration.
Trusted by engineering teams
5.0
What we engineer
Production AI systems built on LangChain, LlamaIndex, OpenAI, Anthropic, AWS Bedrock, and open-source models (Mistral, Llama).
RAG Systems & Knowledge Assistants
Domain-specific AI assistants grounded in your internal data. We engineer retrieval pipelines (dense + sparse, re-ranking, hybrid search), chunking strategies, and eval frameworks — not just a wrapper around a chat API.
Learn more →Autonomous AI Agents
Multi-step agents that automate complex workflows: document processing, data extraction, decision routing, and API orchestration — with guardrails and observability built in.
Learn more →LLM Fine-tuning & ML
Custom model training, fine-tuning (LoRA/QLoRA), and predictive ML. When a general-purpose model isn't enough for your domain — we build what fits.
Learn more →Workflow Automation
AI-powered automation integrated with your existing stack. NLP and generative AI applied to operational processes — document handling, CRM enrichment, reporting pipelines.
Learn more →Our approach
We map your processes before touching a model. Each engagement is scoped around measurable outcomes — not AI for its own sake.
AI assessment and process mapping
We run structured discovery sessions with your team — engineering, ops, and domain experts. We map workflows, identify friction points, and locate where AI creates measurable leverage versus where it adds complexity.
Output: a prioritized use-case roadmap, with time-to-value and effort estimates for each item.
High-impact use-case selection
We score use cases against a framework combining time savings, technical feasibility, and integration cost — benchmarked against patterns from live client deployments. Your engineering budget concentrates on the highest-ROI items first.
Your AI investment targets what actually moves the needle.
Iterative build and production deployment
We ship functional prototypes fast, validate with real users, then harden for production: API integrations, observability, error handling, cost controls. No "demo mode" deliverables — everything is built to run in your environment.
Solutions are validated on real data before full rollout — protecting your investment.
Ongoing support and iteration
Delivery is not the finish line. We monitor performance, handle model drift, and iterate based on production feedback. Documentation, runbooks, and knowledge transfer are included — your team owns what we build together.
Your team stays in control and gains compound ROI over time.
Services
We engage at every stage — from strategic clarity through production delivery. Remote-first, async-friendly, built for technical teams.
AI Audit and Strategy
We identify the real value levers in your organization — not a theoretical framework, but a scoped analysis of your existing processes, data, and team capabilities. Output: a prioritized roadmap with cost/time estimates and a clear build-vs-buy recommendation for each initiative.
Our methodology draws on benchmarks from delivered client projects and is calibrated to your technical stack and engineering capacity. No vendor lock-in, no upsell pressure.
- Structured process interviews across engineering, ops, and leadership
- Use-case scoring against live benchmarks from similar deployments
- Prioritized roadmap with effort, ROI, and sequencing
RAG Systems and Internal AI Assistants
We build knowledge assistants that actually work at scale — handling thousands of documents, complex queries, and production traffic. Stack: LangChain or LlamaIndex, Pinecone / Weaviate / pgvector, OpenAI or Anthropic, with hybrid retrieval and evaluation pipelines (RAGAS, custom evals).
Every RAG system we ship includes: chunking strategy justified by your data structure, re-ranking layer, confidence scoring, hallucination guardrails, and a monitoring dashboard for retrieval quality over time.
- Production-grade retrieval pipelines (dense + sparse, hybrid search)
- Evaluation framework to measure and improve retrieval accuracy
- Integrates with your existing data sources, auth, and APIs
Custom AI Development
End-to-end engineering of AI-powered applications: agentic pipelines, LLM fine-tuning (LoRA, QLoRA on Mistral or Llama), predictive ML models, and intelligent automation integrated with your existing infrastructure (CRM, ERP, data warehouse).
We work iteratively — functional prototypes first, production hardening second. Each sprint delivers something you can test in your environment. We hand off clean, documented code, not a black box.
- Working prototype in the first sprint — no months of requirements gathering
- Integration with your existing stack — APIs, auth, data pipelines
- Documented handoff so your team can maintain and extend the system
On AI expectations
"AI is not magic. It won't replace your team or 10x your revenue in year one. That's why you start simple, measure concrete returns, and increment from there."
— Anas Rabhi, Founder, Tensoria
Start your AI project
A 30-minute call to assess whether AI creates genuine value for your use case. We give you direct, honest recommendations — no deck, no sales pitch.
Common questions
Direct answers about how we work, what we deliver, and what to expect.
Latest insights
Engineering deep dives
Production patterns, anti-patterns, and trade-offs we've learned shipping LLM systems.
5 Failure Modes We Keep Seeing
REX from production audits. The 5 things that actually break RAG in prod — none of them are chunking.
Read article → AI AgentsMulti-Agent Orchestration Compared
LangGraph vs CrewAI vs AutoGen vs custom. Trade-offs from production deployments.
Read article → RAG + AgentsAgentic RAG: When Retrieval Meets Agents
Self-RAG, Corrective RAG, ReAct retrieval, query decomposition — when agentic patterns earn their cost.
Read article → RAG FundamentalsRAG: A Technical Guide
Architecture, embeddings, retrieval strategies, evaluation. The technical reference we link to.
Read article →