Engineering™ · AI & Automation

AI Workflow Engineering

We engineer hybrid intelligent workflows combining people, automations, AI models, and agents.

Orchestrated workflows where each step is executed by the optimal component: deterministic code for absolute precision, LLMs for synthesis, autonomous agents for dynamic tasks, and human experts for final sign-off.

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Scope

Hybrid Human+AI Workflows · Task-Component Mapping · Stateful Orchestration · Cost & Latency Optimization

Estimated Timeline

5–8 weeks

Platforms

LangGraph / Temporal · n8n / Python · Claude / OpenAI · Internal APIs

Target Fit

Is this for your company?

This service is for you if

✓Your team uses ChatGPT haphazardly, resulting in inconsistent quality and non-standard outputs.
✓You manage multi-step processes combining data extraction, technical report generation, and expert human review.
✓You want to amplify expert output by 5x while maintaining full quality control and human oversight.
✓You need to optimize token costs and latency by routing queries across small specialized models versus frontier LLMs.

You probably do not need it if

✕Your process can be resolved with standard linear automation without any cognitive processing.
✕You aim to eliminate human involvement in tasks requiring ethical, legal, or high-liability accountability.

Problem Space

What we solve

Optimal Task-to-Component Allocation

Decomposing processes so deterministic code handles math, LLMs draft text, and humans approve final output.

Consistent & Deterministic Outputs

Enforcing strict JSON structured outputs ensuring AI responses fit cleanly into downstream databases.

Cost & Latency Model Routing

Intelligent routing using fast, cost-effective models for filtering and reserving frontier LLMs for deep reasoning.

Stateful Long-Running Process Orchestration

Stateful workflow engines capable of pausing hours or days for human approval before resuming execution.

Engineering Process

How it works

01 — Diagnose

Task Audit & Cognitive Breakdown

We map operational workflows into micro-tasks: deterministic, generative, analytical, and critical judgment.

02 — Design

Hybrid Orchestration Architecture

We blueprint component assignments, design human review interfaces, and establish data interchange schemas.

03 — Build

Workflow Coding & LLM Integration

We engineer stateful graphs with Temporal or LangGraph, enforce structured outputs, and validate latency.

04 — Launch

Team Enablement & Copilot Rollout

We train your team to operate the new copilot workflow and track end-to-end productivity benchmarks.

Deliverables

What we deliver

Upon completion you will have

✓Production-ready hybrid workflow fully orchestrated and documented.

✓Model routing middleware with automated token cost and latency optimization.

✓Schema validation layer enforcing typed JSON outputs (Pydantic / Zod).

✓Human-in-the-loop review checkpoints integrated into Slack, email, or web portals.

✓Operational performance metrics tracking cycle time reduction and billable hours saved.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:5–8 weeks

Week 1Phase 1

Task Mapping & Component Allocation

Classifying human, algorithmic, and cognitive tasks; defining sign-off checkpoints.

Weeks 2–4Phase 2

Orchestration Engine & Structured Prompts

State machine development, structured outputs, validation layers, and LLM API connections.

Weeks 5–6Phase 3

Human Review Interfaces & Tooling

Building reviewer UI, notification webhooks, and operational database integrations.

Weeks 7–8Phase 4

Pilot Runs & Production Cutover

Supervised operation with team members, prompt tuning, and velocity benchmarking.

Pricing Guidance

Estimated Investment

Target Investment

USD 5,500

Includes process decomposition, stateful orchestration architecture, schema validation, and review interfaces.

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Real-World Proof

Impact Case Study

From 6 Hours to 22 Minutes for Complex B2B RFP Proposals

Initial problem

Engineering consultancy where senior directors spent 6 hours drafting technical proposals from 80-page tender documents.

Technical intervention

Engineering of a hybrid workflow: vision models parse technical requirements, vector agents retrieve past project specs, an LLM drafts the technical core, and the director conducts final review.

Outcome achieved

Proposal creation time dropped to 22 minutes, allowing a 5x surge in monthly tender submissions with zero drop in technical quality.

Clarifications

Frequently asked questions

How does an AI Workflow differ from a standalone AI Agent?

A standalone agent attempts to complete an entire task end-to-end using tools. An AI Workflow is a broader orchestration system coordinating deterministic code, multiple LLMs, API microservices, and human checkpoints.

How do you guarantee the AI always outputs the required schema format?

We enforce native Structured Outputs (JSON schema enforcement) and runtime schema validation with typed libraries (Pydantic / Zod). If an output is malformed, it self-corrects before proceeding downstream.

What cloud infrastructure runs these workflows?

We deploy on state-of-the-art workflow engines like Temporal or n8n Enterprise in your private cloud, ensuring that if a server restarts mid-process, the workflow resumes exactly where it paused.

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