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Lead capability

Agentic AI development

We design and build AI agents that carry real work from source documents to a finished deliverable, then hand it to your team to review and send.

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How an agent works

We build agents that do the work, rather than chat windows that only describe it.

They read a company’s documents and data, run a pipeline, and hand back a deliverable. Each runs in a secure harness, on AI models that protect proprietary data.

  1. 01

    Read the source

    Specs, drawings, contracts, email, and the systems your team already keeps.

  2. 02

    Extract the work

    Every requirement, line item, and deadline, pulled out and organized.

  3. 03

    Draft the deliverable

    A bid scope, proposal, schedule, or report, written from your own past work.

  4. 04

    Check and hand it off

    Cross-checked against the source, then delivered as Word, Excel, PDF, or into your tools.

Use cases

Work that agents take on

These are pipelines we built and run in production for construction companies. The same approach fits any operation that runs on documents.

  • Bid scopes

    A division-by-division scope of work built from the specs, drawings, and addenda.

  • RFP responses

    Every requirement extracted, and a response drafted section by section from past proposals.

  • Submittal reviews

    A submittal checked for compliance against the controlling spec section.

  • Schedules of values

    An AIA G703-compatible schedule of values with a cash flow projection.

  • CPM schedules

    A critical path schedule exported for Primavera P6 and Microsoft Project.

  • Safety audits

    An OSHA-cited site audit scored from jobsite photos, with corrective actions assigned.

Connected to the tools you use

  • The Site Specs construction platform
  • Microsoft SharePoint document libraries
  • Microsoft Outlook email
  • Sourced research on the live web

Delivered as standard files

  • Word
  • Excel
  • PDF
  • Primavera P6 (.xer)
  • Microsoft Project

Security

Built to protect proprietary data

Agents work with a company’s most sensitive documents, so we build for enterprise security from the start.

  • Secure agent harnesses

    Each agent runs inside a harness we build for enterprise security: the software around the model that supplies its documents and tools and runs each step.

  • Proprietary data protected

    Agents run on AI models that protect a company’s proprietary data, from the documents they read to the deliverables they produce.

  • Siloed knowledge bases

    Each company’s knowledge base, including what its agents learn over time, is siloed to that company and its users.

How we work

How an engagement runs

  1. 01

    Discover

    We learn the operation, the documents behind it, and what a finished result looks like.

  2. 02

    Design and build

    We design the interface and build the system with your team, in short steps you can review.

  3. 03

    Deploy and support

    We launch it, watch it in use, and stay available to extend it.

In production

SiteSpecs AI homepage with the headline “Put an expert AI agent on every crew” and status cards for bid scope, RFP, and safety agents

Agentic AI · Construction

SiteSpecs AI

Expert AI agents and multi-step pipelines that read a contractor’s documents and return finished bid scopes, RFP responses, schedules, and safety audits.

View case study

Questions about agentic AI development

What is agentic AI development?

It is the work of building AI agents that carry out multi-step tasks on their own. An agent reads source material, decides the next step, uses the tools and systems it is connected to, and returns a finished result for a person to review.

How is an AI agent different from a chatbot?

A chatbot answers questions in a conversation. An agent runs a job. It reads a spec book or a mailbox, extracts what matters, drafts a deliverable such as a bid scope or a report, checks it against the source, and saves the file where your team works.

What kind of work suits an agent?

Repetitive, document-heavy work with a clear finished product, such as bid scopes, RFP responses, submittal reviews, schedules, safety audits, and reports. If a task has several steps and an expert can describe what done looks like, it is a good candidate for a pipeline.

What systems can the agents connect to?

The agents we built for SiteSpecs AI work inside the Site Specs platform, Microsoft SharePoint, and Outlook, and run sourced research on the live web. On a new project we connect them to the systems your team already uses.

How do you protect our proprietary data?

Every agent runs inside a secure harness we build, on AI models that protect a company’s proprietary data. Each company’s knowledge base, including what its agents learn, is siloed to that company and its users.

What is an agent harness?

A harness is the software around an AI model that turns it into an agent. It gives the agent its documents and tools, runs each step of the pipeline, checks the result against the source, and hands it off. We build ours for enterprise security.

What do the agents produce?

Standard files your team can open anywhere, including Word documents, Excel workbooks, PDFs, Primavera P6 schedules, and Microsoft Project files.

How does a project start?

With a conversation about the work and the documents behind it. We then design and build the agents with your team in short steps you can review, launch them, and stay available to extend them.

Who is Obvious Ridge Route?

Obvious Ridge Route is a software studio in Chicago, Illinois, founded in 2013 by Mike Reilly. It designs and builds secure agentic AI systems and production software, and works with teams remotely.

Have work an agent could take on?

Tell us about the documents and the finished result, and we will reply by email.

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