All Services AI Deployment Sprint

Deploy One Practical AI Workflow in 30 Days

Q: How long does the AI Deployment Sprint take?

The standard sprint runs about 30 days and ends with a working AI pilot plus a production roadmap. Smaller, single-workflow use cases can finish in two weeks; more advanced pilots with deeper integration and governance run closer to six weeks or move into a separately scoped production phase.

Q: Is this a workshop or a real implementation?

An implementation. Discovery and design are included, but the outcome is a working AI pilot running on AWS — not a slide deck. Unlike an open-ended workshop, the scope is fixed: one workflow, one primary data source, one measurable outcome.

Q: How is this different from your Generative AI service?

The AI Deployment Sprint is the fixed-price front door — a fast, low-risk way to prove one use case and leave with a production roadmap. The Generative AI practice is where you go next to build broadly, harden for scale, and operate in production. Think of the Sprint as the on-ramp and Generative AI as the highway.

Q: Do we need to be on AWS already?

AWS is preferred, because the sprint is designed around AWS-native AI services like Amazon Bedrock, Lambda, and Amazon Connect. If you are not on AWS yet, we can still evaluate whether an AWS-based pilot makes sense and scope accordingly.

Q: What access do you need?

It depends on the use case, but usually a business sponsor, a technical contact, a selected data source or sample documents, and access to an approved AWS or test environment. We avoid unnecessary access and define clear boundaries before any implementation begins.

Q: Will you modify our production systems?

Not without explicit approval. The sprint is built to minimize production risk. Most pilots start in a controlled environment using approved data sources and limited, read-only access — nothing changes in production without your sign-off.

Q: Can you guarantee ROI?

No responsible provider guarantees ROI before seeing your workflow and data. What we do is establish a baseline metric, build the pilot, measure against it, and give you an honest read on whether the use case deserves further investment. The point of a fixed-price sprint is to learn that cheaply.

Q: Is this only for chatbots?

No. Chat is one interface. The sprint can deliver internal knowledge search, document processing, call summarization and agent assist, operational reporting, and workflow automation — across text and voice.

Q: What happens after the sprint?

If the pilot proves value, we can move it into production, run it as a managed AI operation, expand it into contact-center and voice AI on Amazon Connect, or plan a broader AI roadmap. The sprint is designed to make that next decision obvious and low-risk.

Most companies aren't short on AI ideas — they're stuck between experiments, vendor demos, and unclear ROI. The industry's answer in 2026 is forward-deployed engineering: put engineers next to the team and ship. Stratto brings that model to the mid-market as a fixed-price sprint — we embed with your team, take one high-value workflow from idea to a working AWS-native pilot, add governance, and leave you with a production roadmap and a team that can run it.

Schedule a Fit Call

Fixed-price sprint working pilot in ~30 days

The Problem

AI Interest Is High. Real Deployment Is Slow.

Most teams have already tested ChatGPT, Copilot, or an internal experiment. The problem isn't curiosity — it's execution. Deciding which use case matters, finding where the data lives, securing access, and proving ROI is where AI stalls, and pilots quietly die before production.

No Clear First Use Case

Teams know they should be using AI but can't agree which workflow is worth automating first — so nothing ships. Data is often scattered across tickets, docs, call notes, and spreadsheets with inconsistent access.

Security Slows Everything

Leaders want AI but not uncontrolled data exposure, hallucinated answers, or shadow IT. Without guardrails and clear boundaries from day one, projects stay stuck in review instead of production.

Overloaded Teams, Unproven ROI

IT and operations already have a backlog, so AI gets discussed, tested, and delayed. And without a baseline metric, it stays a technology experiment instead of a business improvement anyone can defend.

Our Approach

Forward-Deployed Engineering, Mid-Market Priced

Stratto Technologies embeds engineers with your team for one focused sprint — analyzing the workflow, designing the AWS architecture, building the pilot, and setting up governance. The goal isn't a demo you admire and abandon; it's a working use case, a measurable result, and a team that can carry it forward without us.

In 2026, AWS, Anthropic, and OpenAI all reached the same conclusion: the bottleneck to enterprise AI isn't the model — it's implementation. Their answer was forward-deployed engineering: put senior engineers directly alongside the customer's team and compress the distance between ambition and production.

We productized that model for companies without an enterprise budget. One workflow. One primary data source. One measurable outcome. A fixed price, so the scope stays honest.

Typical AI Exploration

— Open-ended workshops

— Generic AI demos on sample data

— Unclear ownership after kickoff

— No path from demo to production

— Limited governance and access control

— Hard to measure — no baseline

The Sprint

Stratto AI Deployment Sprint

Fixed-price, fixed-scope sprint

One real business workflow, your data

Named business and technical owners

A working pilot + 30/60/90 roadmap

Security, logging, access, and escalation built in

ROI model and success metrics

What's Included

A Working Pilot — and a Clear Path to Production

Every sprint is engineered to produce a functional AI pilot on AWS plus everything your team needs to take it further: use-case selection, workflow design, secure architecture, governance controls, an ROI model, and a 30/60/90-day roadmap.

Use-Case Selection

We score candidate workflows on business value, feasibility, data readiness, risk, and speed to value — then pick the one worth building first.

Workflow Design

We map the current process, define the AI-enabled version, and make explicit where humans stay in control of the decision.

AWS-Native Architecture

A solution designed on services like Amazon Bedrock, Lambda, S3, DynamoDB, Amazon Connect, Lex, and OpenSearch — chosen for your workflow and risk profile.

Working AI Pilot

A functional pilot for one workflow, connected to an approved data source and tested on real examples. The output is software, not a slide deck.

Security & Governance

Access boundaries, logging, escalation rules, human-review points, and data-handling assumptions — defined before we build, not bolted on after.

ROI Model & Roadmap

A baseline metric, an estimate of the business value, and a 30/60/90-day roadmap for production rollout, integrations, and managed operations.

How It Works

One Use Case. One Workflow. One Measurable Outcome.

The sprint is intentionally limited — we don't try to transform the whole company at once. Five stages take you from stakeholder interviews to a validated pilot and a production plan in about 30 days.

Days 1–3

Discover

Interview stakeholders, review the workflow, inventory data sources and systems.

Days 4–5

Select

Score use cases and pick the strongest by value, feasibility, and manageable risk.

Days 6–8

Design

Design the workflow, AWS architecture, access model, and governance controls.

Days 9–18

Build

Implement the pilot, connect the data source, test responses, prepare the demo.

Days 19–20

Validate & Handoff

Test real examples, train your team, deliver the roadmap and handoff package.

Use Cases

AI Workflows We Can Deploy First

The best first use cases are specific, measurable, and supported by data you already have. Stratto prioritizes workflows where the risk is manageable and the value is easy to explain — across both text and voice, on AWS-native services.

Customer Service AI Assistant

Answer repetitive questions from approved documents, policies, FAQs, and support content.

Call Summarization & Agent Assist

Generate call summaries, disposition notes, next steps, and suggested responses for service teams.

Internal Knowledge Assistant

Let employees search SOPs, policies, and product docs through natural language.

Sales Workflow Assistant

Support lead qualification, follow-up drafting, account research, and CRM summaries.

Document Processing Assistant

Summarize, classify, extract, and route information from forms, emails, PDFs, and records.

Field & Ops Assistant

Give technicians and ops teams instant access to procedures, troubleshooting, and asset info.

Amazon BedrockAmazon ConnectAmazon LexAWS LambdaS3 & DynamoDBOpenSearchCognito & KMSCloudWatch & CloudTrail

Governance

AI With Boundaries, Logging, and Human Control

Useful AI cannot be uncontrolled AI. Every sprint ships with practical governance so you know exactly what the AI can access, what it can do, where humans stay in the loop, and how activity is logged.

Role-based access

Data-source boundaries

Human escalation rules

Logging and audit trail

Source-aware responses

Prompt & config documentation

Failure & fallback behavior

Cost-monitoring assumptions

Our Policy

The AI system is designed to assist users by retrieving, summarizing, drafting, classifying, or recommending actions based on approved data sources. It does not replace human judgment for legal, financial, medical, safety-critical, or contractual decisions unless separately reviewed and approved under a formal governance process.

It's the same discipline we bring to our own platforms — the opposite of vibe-coding AI into production.

Who It's For

Is This Right for Your Team?

The AI Deployment Sprint is built for organizations ready to move past experimentation and test one real workflow with a controlled, fixed-price implementation.

Good Fit

Mid-market teams with repetitive workflows

Running production workloads on AWS

Service or ops teams with high manual effort

Useful docs, tickets, or call notes on hand

Leaders who want a fixed-price way to prove value

Not a Good Fit

A general AI brainstorming session only

Teams without access to useful data

Complex multi-system integration in sprint one

High-risk autonomous decisioning

No business owner for the workflow

Target Outcomes

20–40%

less time on repetitive tasks

30–60%

less time spent searching for answers

~30 days

to a working, validated pilot

30/60/90

day roadmap to production

Illustrative ranges. Every sprint defines its own baseline and success metric up front.

Engagement

Fixed Price. Fixed Scope.

The sprint is fixed-price because the scope is intentionally limited: one use case, one workflow, one primary data source, one measurable outcome. Three package sizes match the depth of integration you need — pricing is shared on a short fit call.

Sprint Lite

2 weeks

A simple prototype or executive proof point on one workflow.

Deployment Sprint

4 weeks

A standard working pilot with light integration. The typical starting point.

Production Sprint

6 weeks

A more advanced pilot with deeper integration and governance.

Not sure which fits? A short fit call is the fastest way to scope the workflow, confirm data readiness, and price the sprint. Book one here.

What's Next

From Pilot to Production

The sprint gives you a working pilot and a clear path forward. If the use case proves value, Stratto continues with the practice, operations, or platform that fits — no restart, same team.

FAQ

Common Questions

What mid-market teams ask before running an AI Deployment Sprint with Stratto Technologies.

How long does the AI Deployment Sprint take?

Is this a workshop or a real implementation?

How is this different from your Generative AI service?

Do we need to be on AWS already?

What access do you need?

Will you modify our production systems?

Can you guarantee ROI?

Is this only for chatbots?

What happens after the sprint?

Ready to deploy one practical AI workflow?

One workflow. One use case. One measurable outcome. If your team is serious about AI but needs a practical starting point, the fit call is where it begins.

Schedule a Fit Call Talk to an Engineer