OutcomeOps FAQ

OutcomeOps is an enterprise-controlled AI code generation platform that deploys into your AWS account. Unlike general-purpose coding assistants such as Copilot, Cursor, or Tabnine, OutcomeOps is purpose-built for regulated industries where source code, intellectual property, and architectural standards cannot leave your environment.

The platform indexes your Architecture Decision Records, code maps, Confluence pages, and Jira issues into workspace-scoped knowledge bases, then generates code that already fits the standards your organization has already decided.

Copilot is a vendor product with strong enterprise controls: SOC 2, BAA availability, retention policies. The security review your team runs on Copilot is essentially: can we trust Microsoft with our code?

OutcomeOps deploys via Terraform into your AWS account. Code never leaves your VPC. The security review becomes: can we allow our existing AWS account to do this? — which is a question you have likely already answered.

For most companies, Copilot on its own is fine. For regulated enterprises in healthcare, defense, aerospace, and finance — where architectural compliance matters more than vendor compliance — OutcomeOps is purpose-built.

And if your team is already using Copilot, OutcomeOps integrates with it rather than replacing it. We surface your organization-specific context — security standards, architectural decisions, design patterns — into Copilot’s generation. The suggestions stop looking like generic, internet-trained boilerplate and start looking like code your senior engineers would have written.

An air-gapped system operates entirely within a secure, disconnected network — physically and electronically isolated, with no inbound or outbound connections to the public internet, third-party cloud services, or external APIs.

OutcomeOps supports air-gapped deployment patterns for environments such as:

• Sensitive Compartmented Information Facilities (SCIFs)
• ITAR-restricted defense and aerospace programs
• HIPAA-regulated healthcare environments
• Financial services SOX compliance perimeters
• Sovereign-cloud and AWS GovCloud environments

Model inference, context retrieval, and code generation all happen inside your environment. No external API calls. No vendor dependencies. No data egress. For more on the deployment patterns that make this possible, see the Air-Gapped AI Coding for Defense and Aerospace post.

OutcomeOps redacts personally identifiable information — names, addresses, phone numbers, SSNs, and account numbers — from prompts before they reach a foundation model. Detection runs inside your AWS account; redacted text is what the model sees.

Because this is a developer platform, certain technical identifiers are allow-listed and pass through unredacted by default: IP addresses, URLs, dates and timestamps, and MAC addresses. Without that, normal infrastructure code — logs, configs, network diagrams — would come back unusable.

Redaction is enabled by default on every workspace. Workspace owners can disable it for specific workspaces (for example, when generating code that intentionally manipulates PII fields), but every disable is recorded in the audit log with the user, timestamp, and workspace.

For HIPAA, GDPR, and CCPA postures, this means PHI/PII never appears in foundation-model prompts unless an authorized owner has explicitly opted out for that workspace and accepted the auditable record.

Yes. OutcomeOps uses AWS Bedrock (Anthropic Claude family) by default and can be configured to call Azure OpenAI or other endpoints when required by deployment constraints.

AWS Bedrock has zero data retention for foundation-model invocations by default — AWS does not store your prompts or completions, and does not use them to train foundation models. Combined with the OutcomeOps deployment model (everything runs in your account), this means your code and context never leave your environment.

Because OutcomeOps deploys into your AWS account, it inherits your existing compliance posture. No separate vendor security assessment is required.

OutcomeOps indexes your Architecture Decision Records, code maps, Confluence pages, and Jira issues into workspace-scoped knowledge bases. When the platform generates code, it queries the relevant standards on every chunk of output.

Every pull request the platform produces includes:

• Compliance check against the relevant ADRs
• License scan to flag GPL or other restricted dependencies
• Architectural duplication detection
• Self-review pass against the falsifiable rules in your standards
• Human approval required before merge

The platform itself is the guardrail. For the deeper argument on how ADRs become load-bearing infrastructure when AI is the executor, see How One ADR Got Claude to Stop Making the Same Mistake.

Tabnine is an AI coding assistant focused on developer-facing experiences — code completion, chat, and agents inside the IDE — with on-prem and air-gapped deployment options for security-sensitive environments.

OutcomeOps is an AI code generation platform focused on organizational context and governance. It generates entire features from Jira tickets, enforces ADRs at chunk-time, and routes every change through a self-review and human PR approval flow.

Tabnine helps developers write code faster. OutcomeOps helps enterprises enforce standards at scale. They solve different problems — some customers use both.

OutcomeOps deploys via Terraform in roughly 30–60 minutes. A typical first week looks like:

• Day 1: Deploy infrastructure (Lambda, S3, DynamoDB, KMS, S3 Vectors, Bedrock)
• Day 2: Index your ADRs, code maps, Confluence, and Jira
• Day 3: Make your codebase queryable as architecture, then generate the first PR from a real Jira ticket. (See Self-Documenting Architecture: When Code Becomes Queryable.)

Because the deployment is Terraform inside your AWS account, regulated-enterprise security teams typically approve the architecture in their first review — the deployment model eliminates the vendor-data-flow questions that stall SaaS-first procurements.

Yes. We offer a 30-day proof of value inside your AWS account.

You deploy OutcomeOps via Terraform, index your ADRs and code, and generate 5–10 PRs from real Jira tickets. At the end of 30 days, you have:

• A working deployment in your own AWS account
• Real PRs to evaluate against your own quality bar
• Audit logs showing compliance with your ADRs
• Cost metrics — typically $2–$4 per generated feature, with simpler tasks running well under a dollar

If it does not work for your team, you delete the Terraform stack. There is no vendor lock-in because there is no vendor environment to lock you into.

Yes. OutcomeOps is available on AWS Marketplace for simplified procurement. This means:

• Purchase through your existing AWS account
• Charges appear on your AWS bill (no new vendor)
• AWS standard terms (no custom contract negotiation)
• Faster procurement — typically 2–4 weeks vs. 90–180 days for a new-vendor cycle

For organizations that have committed AWS spend (EDP, PPA), OutcomeOps drawdown can apply to that commit.

Both. OutcomeOps runs two retrieval modes with a small classifier picking between them per query:

• Code-map RAG — LLM-summarized code maps + ADRs, embedded and stored in S3 Vectors. Best for narrative/architectural questions like "how does the order-fulfillment pipeline work?"
• Code knowledge graph — AST-derived nodes (files, classes, functions) and typed edges (calls, inherits-from, imports). Best for symbol-level questions like "every caller of this shared library function" — where completeness matters and a summary cannot promise it.
• Router — a per-query classifier picks RAG, the graph, or both. Engineers never see the routing.

The architecture details and the reasoning are in Why RAG Isn’t Enough for Code: Adding a Graph.

When completeness matters — when the question is about a specific symbol and the answer needs to be exhaustive, not approximate. Examples:

• Refactoring a shared library function — the router pulls the graph to enumerate every caller before the model proposes the change.
• Adding a required method to a base class — the graph returns every subclass.
• PR structural review — the diff-impact pass uses the graph to surface every consumer of every changed symbol.
• Code-generation impact analysis — before generating new code, the graph identifies what callers will break.

For architectural questions (how does X work, which area handles Y), the router uses RAG with code-maps. For hybrid questions (why is X shaped this way and what calls it today), it uses both and combines the answers.

Yes. OutcomeOps deploys as Terraform into two AWS regions of your choosing, active-active. Both regions are fully deployed and continuously serving the same data — there is no passive standby that has to wake up during failover.

• Lambda dual-writes to DynamoDB and S3 Vectors in both regions before any ingestion job acknowledges. RPO is effectively zero for committed data.
• Two stable per-region endpoints on your internal DNS — no Route 53 dependency, since most enterprise customers run their own internal DNS.
• AWS AppConfig per-region flag prevents scheduled jobs from running twice when EventBridge fires in both regions.
• No managed cross-region services in the data path — no DynamoDB Global Tables, no third-party replication pipeline, no new entries on your SOC 2 / HIPAA sub-processor list.

Architecture deep-dive: Why OutcomeOps Doesn’t Use DynamoDB Global Tables.

The customer points users to the surviving region. That can be a DNS A-record change on your internal DNS, a Slack or MS Teams announcement to switch endpoints, or both. The data is already there — Lambda dual-writes mean every DynamoDB write and every S3 Vector update happened in both regions at ingestion time.

Failover is human-in-the-loop by design. We don’t want a routing decision to take down a customer’s system because of a transient health-check flap, and we’ve seen enough “global” AWS control planes fail during the worst kind of event to be skeptical of automatic cross-region routing services.

• RTO ≈ seconds-to-minutes, customer-controlled. A Slack/Teams endpoint announcement is near-zero. A DNS A-record flip is bounded by your internal-DNS TTL (5–15 min).
• RPO ≈ 0 for any acknowledged ingestion. Worst case ≈ one ingestion interval (typically 1 hour) on rare asymmetric dual-write failures, which the next scheduled run reconciles.
• OutcomeOps personnel are not in the failover path.

The motivating principle: the platform that holds the map of how your systems work has to stay up precisely when those systems are misbehaving — because that’s exactly when teams need the map.