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Enterprise AI engineering

Production AI for complex enterprise systems.

Agentic systems, enterprise AI and the data platforms behind them — engineered to reach production, backed by three decades of enterprise systems engineering.

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Discuss an AI or data initiative.
02AI capabilities

Engineering for successful AI initiatives.

We isolate the statistical component that decides the outcome, prove it against measurable thresholds, then engineer the application, data and operating layers around evidence. Production AI since 2021 — on enterprise foundations built since 1991.

01 / capability

Agentic AI & enterprise applications

Tool-using agents and AI-enabled applications designed around enterprise workflows, integration boundaries and meaningful human oversight.

02 / capability

Enterprise chat, knowledge & document intelligence

Retrieval, RAG, search and document intelligence engineered for the reliability, evaluation and access patterns enterprise knowledge demands.

03 / capability

AI-powered data, ML & predictive systems

Data products, machine learning and predictive systems built on usable data foundations, with deep learning applied where it is the right tool.

04 / capability

AI platforms, MLOps & production architecture

The data, model and application architecture required to evaluate, deploy, observe and operate AI beyond the prototype.

Explore AI capabilitiesExplore Data Engineering
03Problems we solve

Resolve uncertainty before you build.

We apply rigorous engineering discipline from the outset. We resolve the material business, data and AI uncertainties before the POC — so implementation begins with evidence, measurable thresholds and a credible path to production.

  1. 01Prove AI viability before the POC
  2. 02Make unreliable RAG measurable
  3. 03Unify fragmented data for AI
  4. 04Automate with human oversight
  5. 05Recover a blocked technical program
  6. 06Modernize a platform for AI
04Method & foundation

The Effority Method.

Frame the decision. Validate AI viability. Engineer the production system. Verify and evolve it under control.

  1. 00–01frame

    Frame

    Define the AI decision, users, risks and measurable acceptance criteria.

  2. 02–03validate

    Validate

    Verify the data and test AI viability against locked evidence.

  3. 04–06engineer

    Engineer

    Design, prove and build the complete production AI system.

  4. 07–09control

    Verify & evolve

    Test, accept, release and evolve the AI through controlled gates.

Full method / 10 evidence-gated phasesExplore the Effority Method
05Selected work

Products, platforms and enterprise AI.

Three signals from the wider register: a proprietary Effority platform, project recovery and regulated knowledge systems.

Effority product / Agentic enterprise platform

Telynt Agentic Enterprise Platform

A proprietary Effority platform for versioned AI agents, workflows and reusable solutions governed across the delivery lifecycle.

Regulated enterprise / Knowledge systems

Banking document intelligence & RAG

Ingestion, retrieval, tenant isolation, grounded generation and evaluation engineered as one system.

See all selected AI work
06Evidence in one view

Scale, depth and current AI proof.

86+Documented engagements across our engineers' careers
65+Client organizations across our engineers' careers
1991 → nowEnterprise systems since 1991; production AI since 2021
Full suiteDataiku certifications · Snowflake Summit SF 2025 speaking · Sencha MVP recognition among our members
07How engagements work

Enter at the point of highest leverage.

Four ways to bring senior AI and enterprise engineering into the initiative.

08 / Start the conversation

Find the shortest credible path to production.

Discuss an AI or data initiative.