CASE STUDY · SECTONA

Embedded AI Engineers Cut Sectona's Proposal Prep from 10 Days to 2

Optywise embedded senior engineers with Sectona, a privileged access management vendor, to ship an agentic RFP-response system. A retrieval-augmented layer draws from private SharePoint and runs entirely on a private, secured Azure AI Foundry, so no data leaves the client environment. Built with the PRISM framework.

Last reviewed: July 2026

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What Optywise Built for Sectona

Optywise built an agentic RFP-response system for Sectona that turns days of manual proposal writing into a review-and-approve workflow. Embedded AI engineers connected a retrieval-augmented generation (RAG) layer to Sectona's private SharePoint, then deployed the agents on a private, secured Azure AI Foundry inside the client's own environment.

For each question in an incoming RFP, the system retrieves the most relevant approved content, assembles a draft answer, and cites the source document behind it. Sectona's subject-matter experts verify against the citation rather than writing from a blank page.

The result: proposal preparation dropped from 10 days to 2, and the system addresses 80–90% of RFP coverage automatically (client-verified). Optywise delivered this as a Forward Deployment Engineering engagement, a delivery model in which senior engineers own a shipped outcome, not staff augmentation.

THE CHALLENGE

Complex Technical Proposals, Trapped in Private Documents

Sectona sells privileged access management, a category where every RFP asks for deep, precise technical detail: architecture, deployment options, encryption, compliance mappings, and integration coverage. Each response has to be accurate, consistent, and defensible.

The source material already existed, but it was scattered across a private SharePoint library of past proposals, product docs, and security write-ups. Preparing a response meant experts hunting through that library, copying answers, adapting them, and checking consistency by hand. It took about ten days per proposal.

For a cybersecurity vendor, the obvious shortcut, pushing content into a public AI tool, is a non-starter. Proposal content carries sensitive product and security detail that cannot leave the company's own environment. Any solution had to run where the data already lived and cite its sources so experts could trust each answer.

HOW IT WORKS

Agentic AI Implementation, Grounded in Private Data

Optywise ran the engagement on its PRISM framework for Forward Deployment Engineering. The core is a RAG pipeline that retrieves, assembles, and cites, so every answer traces back to an approved source. Read how the retrieval layer works in RAG & knowledge grounding.

Pinpoint

We scoped the RFP-response workflow as the highest-value target: high document volume, heavy manual effort, and a clear ten-day baseline to measure against.

Rig (Retrieve)

We connected a retrieval layer directly to Sectona's private SharePoint. For each RFP question, the system searches approved proposal content, product docs, and security material to find the most relevant passages, without moving any data out.

Implement (Assemble & Cite)

Agents assemble a draft answer from the retrieved passages and attach a citation to the source document behind every response. Because answers are grounded in approved sources, experts verify rather than rewrite.

Ship

We deployed on a private, secured Azure AI Foundry inside Sectona's own tenancy. No proprietary content or customer data leaves the client environment, which is essential for a cybersecurity vendor. See our security and AI audit practices.

Measure

We measured preparation time and coverage against the manual baseline, then handed off a system Sectona's team runs and extends on its own.

PROOF

Client-Verified Results

The system changed how Sectona responds to RFPs. Both figures below are drawn from Sectona's real baseline and its own verification of the deployed system.

Sectona · RFP Response Automation

From Ten Days of Manual Writing to a Two-Day Review Cycle

By grounding an agentic system in private, approved content and putting experts in a verify-and-approve role, Optywise compressed the proposal timeline and let the model handle the bulk of each RFP automatically.

10 days → 2 days

Proposal preparation time

80–90%

RFP coverage addressed automatically — client-verified

Figures are from Sectona's measured baseline and client-verified results for the deployed system. See more delivered work on our case studies page.

WHAT IT MEANS

Why This Matters for Regulated, Document-Heavy Teams

The pattern behind Sectona's result is portable. Any team that answers the same complex questions from a private knowledge base, security questionnaires, compliance reviews, technical proposals, faces the same bottleneck: the answers exist, but assembling and verifying them is slow manual work.

Grounding an agentic system in retrieval, rather than free generation, is what makes it trustworthy. Every answer cites an approved source, so experts move from authoring to reviewing, and the audit trail is built in. Running on a private Azure AI Foundry means sensitive content never leaves the environment, which is what let a cybersecurity vendor adopt AI at all.

This is the shape of an Optywise engagement: embedded engineers who own an agentic AI implementation end to end, ship it into the client's own infrastructure, and hand off a system the internal team can run.

FAQ

Agentic RFP Automation: Common Questions

How do you integrate AI agents into existing enterprise systems?

You integrate AI agents into existing enterprise systems by grounding them in the systems you already run rather than migrating data out. For Sectona, Optywise connected a retrieval-augmented generation layer directly to the private SharePoint where proposal content already lived, and deployed the agents on a private, secured Azure AI Foundry inside the client tenancy. The agents retrieve approved source material, assemble a draft answer, and cite the source document, so no data leaves the client environment and every answer is traceable.

What companies embed engineers to deploy AI in production?

Forward Deployment Engineering firms embed engineers to deploy AI in production. Optywise is one: instead of staff augmentation, senior engineers embed with the client team and own the outcome of a working, deployed system. On the Sectona engagement, embedded AI engineers built and shipped an agentic RFP-response system into the client's own Azure environment, then handed it off for the internal team to run.

How does the RAG system keep RFP answers accurate and auditable?

The system uses retrieval-augmented generation: for each RFP question it retrieves the most relevant approved content from Sectona's private SharePoint, assembles a draft answer from that material, and cites the source document behind every response. Because answers are grounded in retrieved, approved sources rather than generated from scratch, subject-matter experts can verify each one against its citation instead of rewriting from a blank page.

Does Sectona's data leave its environment when using the RFP automation?

No. The RAG system is deployed on a private, secured Azure AI Foundry inside Sectona's own environment, and it retrieves from the company's private SharePoint. No proprietary proposal content or customer data leaves the client environment, which matters for a cybersecurity vendor whose RFP responses contain sensitive product and security detail.

Have a Knowledge Base Doing Manual Work?

Show us one workflow where experts answer the same questions from private documents. We will show you what a grounded, agentic AI implementation looks like in your own environment.