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Entrans
FORWARD DEPLOYED AI ENGINEERING

We engineer outcomes, not stacks.

Entrans Forward Deployed Engineers embed with your teams and use the Entrans Framework to turn your systems, data and people into AI-driven outcomes.

Deploy an FDE team Check your AI-readiness
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Who we work with

Two businesses. One definition of done: the number moved.

Pick the world you run — the section below changes to what an FDE pod owes you, in the measures you already track.

You may not be AI-enabled yet — that's fine. You have an objective: more revenue per visitor, faster order-to-cash, fewer support tickets, a modern core. And a stack that already runs your business. We don't rip it out. An FDE pod deploys inside what you own and moves the number.

  • AI-discoverable: buyers find you when they ask ChatGPT, Claude or Gemini.
  • AI-accurate: every answer grounded in your data, measured by precision, recall and F1.
  • AI-operated: workflows in your GCC and business lines that agents run end to end.

Framework entry: Connect + Ground (layers 01–02) · no rip-and-replace

Check your AI-readiness See how FDE pods work

The Entrans Framework

One framework. Two starting points.

Enterprises usually enter at Connect and Ground. Product companies at Connect, Evaluate and Discover. Your FDE pod picks the entry layer — the framework carries you the rest of the way.

  1. 01

    Connect

    Enterprise

    Agents reach SAP, Salesforce, WhatsApp and your existing APIs — safely, with typed tools.

    Product company

    Your product's APIs become MCP tools that any AI assistant can call.

  2. 02

    Ground

    Enterprise

    Every answer cites your data — policies, catalog, knowledge. No hallucinated facts.

    Product company

    Your product's knowledge ships as structured, agent-consumable context.

  3. 03

    Orchestrate

    Enterprise

    Business processes run through agents with human-in-the-loop where it matters.

    Product company

    Agent-to-agent (A2A) flows orchestrate across your product and partners.

  4. 04

    Evaluate

    Enterprise

    Accuracy is a KPI: precision, recall and F1 gated before every release.

    Product company

    A regression-gated eval harness ships with every model and prompt change.

  5. 05

    Govern

    Enterprise

    Guardrails, audit trails and agent identity that clear security and regulators.

    Product company

    Agent identity and guardrails your customers' CISOs will approve.

  6. 06

    Discover

    Enterprise

    Customers find you when they ask ChatGPT, Claude, Gemini or Grok.

    Product company

    Your product surfaces in AI answers, measured by AEO attribution.

Open the full framework →

From stacks to outcomes

The IT services model sells effort. We sell results.

Old IT services · Headcount billed

Entrans → Outcomes owned

Old IT services · Tickets closed

Entrans → Senior FDE pods embedded

Old IT services · Time & materials

Entrans → Measurable KPIs, fees linked to results

Three outcome pillars

What an FDE team is accountable for.

Get discovered by AI

[X]%

Revenue from AI channels

Illustrative · placeholder

Make your AI accurate

+[X] pts

F1 / precision / recall

Illustrative · placeholder

Run your enterprise on agents

[X] hrs

Saved per month · cycle time −[X]%

Illustrative · placeholder

Service lines

Six ways we move a business measure.

All services →
01 / AGENTIC + MCP

Agentic & MCP Engineering

Your products and internal systems become usable by AI agents.

02 / KNOWLEDGE + DATA

Knowledge & Data Engineering

AI answers grounded in your truth.

03 / EVALUATIONS

AI Quality & Ops (Evaluations)

Measurably more accurate, reliable AI.

04 / GOVERNANCE

AI Governance

AI you can deploy in regulated environments.

05 / MODEL + EDGE

Model & Edge Engineering

Lower cost, lower latency, data stays where it must.

06 / MODERNIZATION

Modernization & Migration

Legacy systems that are AI-ready.

Featured work · illustrative figures

Outcomes, engineered in production.

Commerce · Case study 1/3

Making a mid-market e-commerce brand shoppable inside AI assistants

Strong catalog and APIs, but invisible when shoppers asked ChatGPT, Claude, Gemini, Meta AI or Perplexity for product recommendations. Zero revenue from AI channels.

Read case study

[X]%

Share of AI-assistant answers for target product queries

[X]%

New revenue from AI channels within 90 days

[X] wks

Time to first AI-driven order

How an FDE engagement works

Four steps from brief to business impact.

  1. 01 · Week 1–2

    Discover

    Pick one business measure. Map the systems, data and people that move it.

  2. 02 · Week 3–8

    Build

    Ship MCP tools, agents and grounding into production inside your stack.

  3. 03 · Continuous

    Evaluate

    Golden datasets and regression gates make accuracy a number that only rises.

  4. 04 · Ongoing

    Scale & hand over

    Extend to adjacent workflows, then transfer ownership to your team.

Engineered acrossGoogle CloudAWSAzureOpenAIAnthropic ClaudeMeta AIGeminiGroqMuseCursorMCPA2ALangGraph

Stop building technology. Start engineering results.

Start with an outcome audit. We’ll map the systems, measures and FDE pod needed to move from AI ambition to production impact.

Deploy an FDE team Check your AI-readiness
Deploy an FDE team