How do you decide what's worth automating?

We use an automation scoring model: how rule-bound is the task, how often does it happen, how long does it take, and what's the error rate. High-frequency, rule-based tasks with structured data are the best targets.

Do we need to have clean data before starting?

Not necessarily. Part of the process mapping phase is assessing data quality. Some automations work fine with messy data; others need a cleanup step first. We'll tell you honestly what's needed.

Can you automate tasks in legacy software that doesn't have an API?

Yes. For systems without APIs, we use RPA (robotic process automation), essentially a script that interacts with the UI like a human would. It's less elegant than API integration but works well for stable interfaces.

What AI models do you use in your automations?

Depends on the task. For classification and extraction, we often fine-tune smaller models on your specific data rather than paying OpenAI rates for every inference. For generation tasks (drafting responses, summaries), we use Claude or GPT-4 with structured prompts.

What happens when an automation breaks?

All our automations include error handling, logging, and alerting. When something fails, it fails noisily, your team gets notified immediately and the fallback to manual processing kicks in automatically.

How do you measure the ROI of automation?

We baseline time-per-task and error rate before building. After deployment, we compare against the same metrics. Most clients see full ROI within 3 months on high-frequency processes.

Can you integrate automations with our existing tools like Slack, Jira, or Salesforce?

Yes. Most automations connect to at least 2 to 3 existing systems. We've built integrations with Slack, Jira, GitHub, Salesforce, HubSpot, Zendesk, QuickBooks, and most common SaaS platforms.

AI Automation

Cut the repetitive work. Ship the important stuff.

We identify the manual, rule-bound tasks eating your engineering team's time and automate them, using the right mix of traditional automation, RPA, and AI where it actually adds value.

Find What to Automate Book a 30-min Call

Free, no commitment. Takes about 30 seconds.

6 wksMedian to production

40%Avg manual work cut

3 daysFirst engineer shortlist

What we do

Automation that's built to last, not to impress.

AI automation isn't about replacing your team, it's about removing the work that slows them down. Data entry that takes 2 hours per day. Reports that nobody reads but everyone compiles. Approval workflows that sit in inboxes. Customer emails that get the same answer 80% of the time. We map these processes, assess automation potential, and build systems that handle them, freeing your team for the work that actually requires judgment.

Engineers placed by EnzRossi, running production systems

What we automate

Common targets across engineering and operations.

Workflow automation

Map and automate multi-step business processes that currently require manual handoffs, status updates, or routing decisions.

Intelligent document processing

Extract structured data from unstructured documents, invoices, contracts, forms, emails, using OCR and classification models.

Conversational AI

Customer-facing or internal chatbots connected to your knowledge base, CRM, or ticketing system. Built to handle real use cases, not demos.

Code review and QA automation

Automated code quality checks, test generation, PR summaries, and regression detection integrated into your CI/CD pipeline.

Reporting and data aggregation

Replace manual report compilation with automated pipelines that pull, transform, and deliver data to the right people on schedule.

Monitoring and alerting automation

Smart alerting systems that reduce alert fatigue by filtering noise, correlating incidents, and routing the right notifications to the right person.

RPA for legacy systems

Robotic Process Automation for software that doesn't have an API, screen scraping, form filling, data migration between old systems.

DevOps task automation

Automate repetitive ops work: environment provisioning, deployment scripts, access management, log parsing, and incident response runbooks.

Our tools

The right tool for each automation layer.

Apache Airflown8nZapier (enterprise)Make.comTemporalPrefectOpenAI APIAnthropic ClaudeLangChainLlamaIndexOllama (local)Hugging FaceTesseract OCRAWS TextractGoogle Document AIAzure Form RecognizerpdfplumberPlaywrightSeleniumPuppeteerUiPath (integration)RobocorpdbtKafkaAirbyteFivetranApache SparkPandasPolarsGitHub ActionsJenkinsTerraformAnsibleAWS LambdaCloud Functions

Real examples

What companies actually automate with us.

Use case 01

Invoice processing pipeline

A logistics company was processing 1,200 invoices per month manually. We built a document pipeline using OCR and a classification model that extracts vendor, amount, line items, and approval routing automatically. Manual processing time dropped from 3 minutes to 15 seconds per invoice.

Use case 02

Customer support triage

A SaaS company's support queue was spending 60% of time on the same 15 question types. We built a classification model that routes inbound tickets and auto-generates first drafts for common issues. First-response time dropped from 4 hours to under 30 minutes.

Use case 03

Engineering onboarding automation

Automated a 22-step manual onboarding process for new engineers: repo access, Jira setup, Slack channels, tool provisioning, and first-day task assignment. Time from offer accepted to day-one ready: from 2 days to 40 minutes.

Use case 04

Weekly business reporting

Finance team spent 6 hours every Monday pulling data from 5 systems into a board report. We built an Airflow pipeline that assembles the report automatically and sends it at 8 AM every Monday. Zero human hours.

Our process

Map it, validate it, build it, measure it.

enzrossi-ai, process

$ step.01

$ step.02

$ step.03

$ step.04

$ step.05

step 01

Process mapping

We spend the first week documenting your highest-cost manual processes. We quantify the time spent, error rate, and business impact for each.

step 02

Automation scoring

Each process gets an automation score based on rule-clarity, data availability, and expected ROI. We prioritize the highest-value targets.

step 03

Architecture and tool selection

We design the automation solution and choose tools based on your existing infrastructure, we don't introduce unnecessary new dependencies.

step 04

Build and test

We build in 2-week sprints with real data from the start. Each automation is validated against edge cases before it touches production.

step 05

Handoff and monitoring

Full documentation, monitoring dashboards, and a 30-day stabilization window. We measure actual time saved against the pre-automation baseline.

Engineers behind these numbers

AI Engineer

ML Engineer

Data Scientist

Full-stack Dev

What clients see

Outcomes from real automation projects.

80%

Avg. time reduction on automated tasks

40 hrs

Avg. hours saved per team per month

99%

Automation reliability after stabilization

3 months

Avg. time to first measurable ROI

Before you decide

The doubts everyone has before hiring us.

And what we do about each one.

AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week one
This will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problem
Our data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
Training in your cloud
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours

FAQ

Questions about AI automation

Find your first win

Tell us the task that wastes the most time.

In a 30-minute call, we can usually identify 2 to 3 high-value automation targets and give you a rough scope.