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ivntechlabs

Ops Automation & Agentic AI

Autonomous workflows that replace repetitive operations. Agentic architectures, validation pipelines, and verified ROI.

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Illustrative example

Workflow example

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Sample input

A prepared inquiry payload

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Field extraction

Map the example fields

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Record preview

Show a proposed record

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Notification preview

Show a draft confirmation

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A local illustration. No AI request, database write, or email is sent.

What we build

Module // 01● Overview

Agentic ops workflows

Multi-step AI agents that run on schedules or event-driven triggers. Ticket triage, automated lead enrichment, scheduling, and internal reports, all fully audited and replayable.

Module // 02● Overview

Document processing & extraction

Invoices, legal contracts, complex forms, and customer email streams. We build structured extraction pipelines with schema validation and human-in-the-loop review for edge cases.

Module // 03● Overview

Data enrichment pipelines

Enrich CRM records, normalize database product catalogs, and deduplicate inventory. Supports batch and streaming modes with strict provider cost caps and rate limits.

Module // 04● Overview

Model-agnostic abstraction

Integrate frontier APIs or self-hosted open-source models. Swap providers dynamically per step in a workflow based on latency, cost, or data-residency needs.

Feature walkthrough
Illustrative exampleReady

Select a feature to play a prepared example of its description.

How a project runs

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// Stage 01 //

Discovery

Two weeks. We map the manual workflow end-to-end, baseline human labor times and error rates, and deliver a clear ROI model before writing code.

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// Stage 02 //

Build

Agent plus evaluation harness from week one. Every change runs against a golden dataset before it reaches production. You see accuracy, cost-per-run, and latency numbers weekly.

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// Stage 03 //

Handoff

Deploy to staging/production, configure dashboards for cost tracking, establish failure mode runbooks, and hand over clean code.

Example project flow
Illustrative exampleReady

Select a stage to explore its role in an example project flow.

Common questions

Common questions
Which models do you use?
Model-agnostic by default. We benchmark GPT, Claude, Gemini, and open-source options against your workload and pick per task. Classification and extraction often run on smaller, cheaper models; reasoning-heavy workflows use frontier models. The abstraction layer means swapping providers is a config change, not a rewrite.
How do you measure automation ROI?
We compare automated throughput and accuracy against your manual baseline (human hours saved). If the system doesn't clear a 3x return within the first quarter, we redesign the pipeline.
How do you handle model drift?
Every production agent is locked to a specific model version and monitored by an automated evaluation suite that runs nightly. Upgrades are promoted only after rigorous testing.
How do you prevent hallucinations in production?
We use JSON schema validation to catch shape errors, retrieval-grounded context rather than model memory, and confidence scoring. If a score falls below the threshold, it triggers human review.
What does it cost?
Automation projects typically land between $30k and $150k for the build plus monthly inference costs. Inference runs $0.01-$0.50 per workflow run depending on model and complexity. We report inference spend weekly so cost never sneaks up.
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Example answer walkthrough
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Open a question to read its answer and see a prepared walkthrough here.

Source: the answers on this page

Ready to automate?

Tell us about the workflow. We respond within 24 hours.

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