Internal knowledge
Knowledge base Q&A
Slack-native Q&A over wiki, runbooks, Confluence. Citations, freshness, KB-gap report.
↓ 2.3 hrs/wk per IC
We build LangChain and LangGraph systems that survive production, with RAG pipelines, full observability, stateful agents, and multi-agent orchestration that hold up when your traffic grows.
Built with tracing, observability, and fallback not a demo that breaks under load.
Document loading, chunking, embedding, retrieval, reranking, prompt injection, generation - with LangSmith tracing at every step. Hybrid retrieval. Citation enforcement. Freshness scoring. Eval harness that catches retrieval degradation before users do.
Multi-step agents with explicit graph structure: tool-use nodes, decision nodes, human-in-the-loop nodes, retry edges, fallback paths. Every edge is a testable transition. Every node has an eval. The agent can be replayed from any checkpoint.
Supervisor agent coordinating specialist sub-agents - each with its own tool set, context and eval. LangGraph manages state handoffs between agents, with explicit memory and checkpointing.
Chains that work on three inputs fail on real volume without observability.
Whoever owns the LLM app: we build for reliability, not just a working demo.
Support copilots, document agents, and review pipelines running live.
Slack-native Q&A over wiki, runbooks, Confluence. Citations, freshness, KB-gap report.
Reads ticket history, product docs, order data. Drafts reply. Routes to human on low confidence.
Reads NDA/MSA, flags clauses, maps against playbook, drafts redlines. Paralegal reviews.
Enrichment → scoring → outreach → CRM sync. Supervised by coordinator agent.
Physician dictation → retrieval over clinical guidelines → SOAP draft → EHR field mapping.
PR → static analysis → LLM review → inline comments → CI gate. Runs on every PR.
Multiple source retrieval → parallel summarisation → synthesis → citation map.
PDF → document loader → extraction chain → validation → ERP write.
Vertex on sovereign GCP region · air-gap options · VPC Service Controls · no internet egress
We map the architecture, build on real data, and ship with full observability.
Map the chain or agent design. LangSmith workspace setup. Eval harness design: golden set per node, regression test plan, prompt version gate.
Chain or graph implementation. LangSmith tracing on every node. Tool integrations. Retry and fallback logic. Eval harness running on golden set.
CI integration for eval gate. Cost monitoring per node. Prompt version management. Production deployment. Runbooks.
LangChain, LangGraph, and the eval and tracing stack that keeps it stable.
Live agentic systems holding up as traffic and complexity grow.
Deep teams with industry context - not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
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LangChain is an open-source framework that helps developers build complex, data-aware applications powered by Large Language Models (LLMs). It connects LLMs with external data sources and computational tools.
A Chain executes a fixed, predefined sequence of steps or actions for consistent workflows. An Agent uses the LLM as a reasoning engine to dynamically decide the sequence of actions and Tools needed.
It implements Retrieval-Augmented Generation (RAG) using Indexes and Retrievers to fetch relevant, proprietary data. This contextual information is then provided to the LLM to ground the response, ensuring accuracy.
Memory modules allow applications to recall context and previous turns within a conversation. This is essential for building sophisticated, stateful conversational agents that deliver a more natural, multi-turn user experience.
LCEL is a declarative syntax that simplifies the composition of complex chains and workflows. It supports advanced features like streaming, parallel processing, and seamless prototyping-to-production deployment.
We implement a pluggable model layer and a modular, component-based design. This architecture prevents vendor lock-in and ensures systems are easily maintainable and extensible for future requirements, lowering TCO.
Fullestop creates autonomous AI agents with secure access to curated internal tools like APIs and databases. This enables the AI to execute complex, multi-step business logic autonomously, turning simple demonstrations into fully functional, scalable applications.
