Agentic AI · Fine-tuning · AI product engineering

We build AI systems
that run in production

Not demos. Agent platforms that plan and use tools, models fine-tuned on your domain, and AI products people actually keep using — shipped with the evaluation and guardrails that make them safe to trust.

See what we've built Start a project
20+
microservices running in production
25+
AI agents orchestrated
6
languages, fully offline-first
10–40%
efficiency gain in production
01 / Platforms we build

Flagship platforms, each solving a different hard problem

These are our own products — architected, built and run end to end. Every one is database-, provider- and model-agnostic by design, and every one is built to production standards: your data stays yours.

01 Flagship platform

ATLAS‑LRO Nexus

A Living Research Operating System

Research papers are static documents describing work nobody can re-run. LRO turns each one into a Living Research Object: claims you can query, a causal graph you can reason over, methods that actually execute in a sandbox, and a cryptographic provenance ledger a regulator can audit. A paper stops being a PDF and becomes a callable function.

  • Executable method runtime — replayable byte-for-byte, with parameter sweeps
  • Causal graph and counterfactual reasoning, not keyword retrieval
  • Tamper-evident provenance ledger built for regulator-grade trust
  • Federated knowledge mesh across publishers, with an LRO exchange
  • Mapped against SOC 2 and HIPAA controls; FDA/EMA/ICH narrative engine
  • 20+ microservices
  • FastAPI
  • Next.js
  • Temporal
  • PostgreSQL
  • pgvector
  • Neo4j
  • Kafka
  • MinIO
  • Docker
  • Helm
  • Kubernetes
02 Enterprise platform

AgentOS

Enterprise agentic AI platform

Multi-agent orchestration with autonomous planning, tool use and multi-step execution. Retrieval runs with or without a vector database, models are swappable through one registry, and the whole platform is configured from the admin UI rather than in code — so it deploys inside your estate, including air-gapped.

  • Model- and provider-agnostic: swap LLMs without touching application code
  • Database-agnostic retrieval — vector, hybrid, or fully vectorless
  • 10+ pre-built agents, plus custom agents authored in the UI
  • Voice in and out; white-label rebrand from one config block
  • FastAPI
  • Python 3.12
  • SQLAlchemy 2.0
  • Next.js 14
  • TypeScript
  • PostgreSQL 16
  • Redis
  • LiteLLM
  • Docker
03 Live in production

Agrozent

Agricultural efficiency platform

AI, traceability and instant payments applied to the agricultural supply chain, lifting efficiency by 10–40%. Yield forecasting and pest detection feed the same system that settles payments to farmers, so a prediction carries a financial consequence rather than sitting in a dashboard.

  • Farm-to-fork traceability
  • Yield forecasting, pest detection, price prediction
  • Real-world-asset tokenisation for working capital
  • Instant settlement to farmers
  • Flask
  • FastAPI
  • PostgreSQL
  • Alembic
  • ML engine
  • Docker
agrozent.com
04 Web + Android & iOS

Haramayn Guide

Offline-first AI pilgrimage companion

Inside Masjid al-Haram the network is unusable, so the entire experience had to work without one. Ritual guidance, duas and a Quran- and Hadith-grounded assistant all run on-device, with phone sensors detecting Tawaf and Sa'i circuits automatically.

  • Step-by-step ritual guidance with context-aware duas
  • Sensor-based Tawaf/Sa'i detection, on-device ML
  • Grounded AI answers — offline engine, API when online
  • Voice in Hindi, Urdu, English, Arabic, Bangla and Tamil
  • Next.js 14 PWA
  • Capacitor 7
  • Turborepo
  • FastAPI
  • Dexie
  • Whisper
  • on-device ML
Our product

AI Lab

Run one prompt across many models at once and compare them on judge scoring, embedding similarity, latency and cost — with API keys encrypted at rest.

  • Flask
  • asyncio
  • LLM-as-judge
  • Fernet encryption
Our product

ZeroCourse

Assessment-first learning: diagnose what someone already knows through adaptive testing, model it on a knowledge graph, then generate only the curriculum they actually need.

  • FastAPI
  • Neo4j
  • PostgreSQL
  • Microservices
Client & collaboration

Procena.ai

Agentic procurement intelligence built with our client's team — 25 configured agents advising on spend, suppliers and contracts, with every monetary figure computed by a deterministic engine.

  • FastAPI
  • TanStack Start
  • PostgreSQL
  • Supabase
procena.ai
02 / What we do

Three things we go deep on

We are not a general software shop. Our work concentrates where AI is hardest to get right — and where getting it right changes the outcome.

Agentic AI systems

Agents that plan, choose tools, recover from failure and finish multi-step work — with the orchestration, memory and permission boundaries that keep them predictable. We have built the platform layer for this ourselves, so we know where autonomy breaks.

  • Multi-agent orchestration & tool use
  • Vectorless and hybrid RAG
  • Long-running task execution
  • Evaluation harnesses & guardrails

Model fine-tuning

When a general model is close but not right, we adapt it to your domain, your vocabulary and your latency and cost budget — and prove the gain against a held-out set rather than a vibe check.

  • Dataset curation & synthetic augmentation
  • LoRA / PEFT adaptation
  • Eval-driven iteration
  • Serving, quantisation, on-device

AI product experience

Most AI features fail on interface, not on model quality. We design for latency, uncertainty and trust — streaming, graceful degradation, visible sources, and an honest answer when the system does not know.

  • Conversational & voice interfaces
  • Offline-first and low-connectivity design
  • Citations & explainability surfaces
  • Human-in-the-loop review flows

Built secure, private and production-grade — not demo-grade

Your data is yours. We build so it stays inside your estate: credentials encrypted at rest, tenant isolation, least-privilege access, audit trails, and deployment on your own infrastructure — including air-gapped — when the data cannot leave. Nothing we build depends on a single vendor: our platforms are database-, provider- and model-agnostic, so you are never locked to one LLM, one cloud or one datastore.

  • Customer data private by design — no training on your data
  • Encryption at rest, tenant isolation, role-based access, audit logging
  • Self-hosted, on-premise or air-gapped deployment
  • Rate limiting, abuse defence and CI quality gates before launch
  • Compliance-aware: SOC 2 and HIPAA control mapping where required
  • Python
  • FastAPI
  • TypeScript
  • Next.js
  • PostgreSQL
  • Neo4j
  • Redis
  • Kafka
  • Docker
  • Kubernetes
  • CI/CD
03 / How we build

Shipping AI is an engineering problem, not a prompt

The gap between a convincing demo and something a business can rely on is measurement, boundaries and operational discipline. This is the method behind every platform we build.

Talk through your problem
  1. 01

    Find the decision, not the feature

    We start from the decision the system has to support and work backwards. That determines what must be deterministic, what can be probabilistic, and where a human stays in the loop.

  2. 02

    Build the evaluation before the model

    A golden corpus and an adversarial critic come first, so quality is measurable from day one. A change that drops a number or weakens an answer fails the gate instead of quietly shipping.

  3. 03

    Constrain what the model is allowed to do

    Engines compute the numbers. Retrieval supplies the facts. The model composes and explains — and anything it asserts without a source gets stripped rather than shown.

  4. 04

    Ship it, then keep it honest

    Deployment, monitoring, rate limits, abuse defence and regression gates. Production is where the real failure modes appear, so that is where the discipline has to hold.

04 / Start

Tell us what the system has to decide.

Send the problem, not a spec. We will tell you honestly whether AI is the right tool for it, and what it would take to run it in production.

Start a project info@emergingx.ai