Demos are easy. Shipping grounded, evaluable AI into real workflows is not. We design RAG pipelines, agentic systems, and Arabic NLP features that sit inside your products and operations—with citations, guardrails, observability, and a path from pilot to production.
In short: we build production RAG, agentic workflows, and Arabic NLP against your real data—with citations, eval harnesses, cost controls, and kill switches— typically in a 4–12 week build sprint from discovery to a handoff-ready pilot.
Engagements are tailored—below are the AI surfaces we ship most often. We work against your real data and workflows so the pilot is measurable, not a generic demo.
Retrieval architecture, chunking and embedding strategy, hybrid search, and citation-backed answers— with an eval harness that catches regressions before your users do.
Idempotent, bounded tool interfaces. Flat multi-agent topologies with versioned contracts. Hard limits on tokens, steps, and spend—not soft warnings that get ignored under load.
Dialect-aware chunking, domain vocabulary, and embeddings tuned for Arabic and mixed Arabic/English content, so retrieval quality doesn’t quietly degrade for your primary market.
Full LLM call logging, tool-call tracing, prompt-injection testing, confirmation checkpoints on irreversible actions, and a kill switch that stops runaway tasks in seconds.
AI engagements run as focused build sprints, four to twelve weeks depending on scope. We audit your data and use case, ship a working pilot fast, then harden it with the guardrails a production system needs before handoff.
Use cases, data sources, success metrics, and constraints—data residency, latency, budget—so we build the right thing first.
A working RAG or agent pilot against your real data, with an eval harness so answer quality is measured, not assumed.
Guardrails, observability, cost controls, and safety testing—prompt injection, resource limits, escalation paths—before go-live.
Runbooks your team can own, or an ongoing managed arrangement if you’d rather we keep operating it.
Model-agnostic—OpenAI, Anthropic, DeepSeek, and self-hosted open-source models—chosen on your latency, cost, and data-residency requirements, not a fixed vendor relationship.
Retrieval indexes and embeddings are built inside your infrastructure or a boundary you control. Data residency is designed in from day one, not bolted on after.
Dialect-aware chunking, domain vocabulary, and bilingual embeddings. Arabic retrieval quality is evaluated with the same rigor as English—not treated as an edge case.
Every answer is grounded with citations back to source documents. Irreversible or high-stakes actions require an explicit confirmation checkpoint—the system fails safely and escalates to a human rather than guessing.
Yes. We routinely work under mutual NDA and can align with your legal template for sensitive architecture and data discussions.
Most discovery, prototyping, and evaluation is effective remotely. We schedule on-site or hybrid workshops in Jordan and the region for kickoff alignment or when sensitive data review benefits from being in the room.
Share your data sources, the workflow you want grounded or automated, and what “good” looks like for accuracy and speed. We’ll propose a sprint shape, an eval plan, and a path from pilot to production.