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  • For regulated engineering teams

AI adoption at

full speed

Most engineering teams are at prompt-in, code-out. Spec Driven Development is a leap ahead.

Anthara takes them there. With team-wide context, governance, and the guardrails required to deliver with speed.

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Is your codebase ready for coding agents?

Works with

Claude Code

Cursor

Codex

Three things AI adoption is stuck on

Engineers have a powerful machine in their hands. Three things keep teams from making the most of it.

  • There is no shared context

Standards, architecture, past decisions. Fragmented across the enterprise stack. Some in tools, most in engineers’ heads. None reach the prompt.

AI builds from what it can see. What it cannot see, it invents. Engineers correct the invention every session.

  • Standards land too late

HIPAA, SOC 2, PCI-DSS. The rule lives in a doc. The code is written at the prompt. They never touch.

Policy is checked at PR review. The cost is paid. The safety net is one human, who cannot read everything.

  • AI adoption is uneven

Software is a team sport. Six engineers using AI six ways add up to zero velocity at the team level.

From discovery to deployment, every team needs one shape for how AI ships. Otherwise the gain stays trapped in pockets.

Three problems.

Higher rework. Elusive productivity gains.

See how Anthara closes all three

[THE REALITY TODAY]

Four things companies are doing today. None of it unlocks adoption.

Tooling. Upskilling. Two governance answers. Each one incomplete.

Tooling

AI coding tools in every engineer's hands. Cursor, Copilot, Claude Code, Codex. Tokens distributed, cost managed. The tools work. They do not know the team.

Upskilling

Training programs, playbooks, internal docs. Some engineers absorb, most do not. The team-level productivity gain never lands.

No formal governance

Most companies have no system. Manual PR review is the only check. The first incident is the wake-up call.

Retrofitted scanners

Snyk, Semgrep, GHAS, Wiz Code. Code scanners retrofitted for AI. They catch issues after the code is written, not at the moment of generation.

Four gaps. None of them connect.

[WHAT IT TAKES]

Adoption and governance. Solved together.

Diagram on what it takes for Adoption and governance getting solved together
Diagram on what it takes for Adoption and governance getting solved together

Engineers do not change how they work. The team moves faster, together.

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With Anthara.
Without the trade-offs.

Same team. Same tools. Three things change.

2–3×

Productivity, team-wide.

Spec Driven Development across the SDLC. Code that aligns with regulatory and internal policies on the first pass.

2×

Fewer iterations to compliant code.

Less rework before merge. Fewer review cycles. Cleaner first drafts.

Day 1

Productive new hires.

New hires start with the team's ways of working already in context. Architecture, standards, gotchas, decisions.

AI speed lands. Without the trade-offs that usually come with it.

The product, in five moments

Five layers between adoption and risk. Each one configured to your team.
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1. The plugin

The team's plugin in every engineer's coding tool. Spec Driven Development from day one. Eleven specialised agents and seventeen skills, ready out of the box.

2. Team-wide context

The team's knowledge served into every AI session. Architecture, standards, past decisions, gotchas, working patterns. Built up as engineers work.

3. Compliance packs

Compliance enforced as code is written, not at PR review. HIPAA, PCI-DSS, WCAG, SOC 2, FDA SaMD, ISO 27001. Or author your own.

4. Gateway and tool governance

Every prompt and every agent action passes through a gateway. Sensitive data redacted before it leaves the boundary. Every tool call checked against the team's rules.

5. Governed agent automation

Automate workflows across Jira, Slack, ServiceNow, Figma, and the rest of the stack. PR reviews, Jira-to-PR, RCAs, CI/CD auto-fixes are common starters. Autonomous or supervised. Full audit trail.

The conduct layer,
up close.

Compliant by construction. Three layers between the engineering team and every AI tool. All inside the network.

  • Team-wide context

The team’s knowledge in every AI session, served over MCP.

  • Policy

Compliance enforced as code is generated. HIPAA, PCI-DSS, WCAG, SOC 2, SaMD, ISO 27001. Pick what applies, or author your own.

  • Gateway

Every prompt, call, and action governed in flight. PHI redacted, MCP controlled at the query level.

Walk Through The Platform

Built for heavily
regulated industries

Healthcare today. Fintech and insurance follow. The conduct layer carries across.

HealthTech

HIPAA and FDA SaMD packs encoded from eighteen years of US healthcare codebases. OCR-defensible audit trail by design.

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FinTech

PCI-DSS, SOC 2, and GLBA enforced where AI generates code. The conduct layer calibrated for financial data and regulatory reporting.

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InsurTech

State-level rules, claims data boundaries, and customer PII redaction. The conduct layer carries across state and federal frameworks.

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See where AI productivity is leaking today

Free Agent Experience Audit. First report in 48 hours. No integration.
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Common questions

Quick answers to what teams ask first.

Still Have Questions? Talk to Us.
What is Anthara?
Anthara is the AI adoption platform for regulated software teams. It puts a code of conduct in the hands of every coding agent. The team’s standards. The industry’s rules. The data boundaries. All held at the moment AI generates code. Engineers ship at full speed, and the work is right the first time.
Regulated software teams of 50 to 250 engineers in healthcare, fintech, govtech, insurtech, and any industry where compliance lives on the code, not on the name on the door. Built for CTOs and VPs of engineering who answer for safe, durable AI across the SDLC.
AI adoption moves through five stages: AI chat, coding assistants, agent mode, multi-agent orchestration, and governed automation. Revenue per engineer rises with depth. So does exposure. Most teams sit at stage two because they cannot review fast enough to safely go deeper.
Anthara’s structural claim. The conduct layer enforces the rules a team has agreed to and the standards an industry requires at the moment AI produces work. It is the infrastructure that makes deep AI adoption durable rather than risky.
Claude Code, Cursor, and Codex today. Anthara writes rule files into each tool’s native format. New tools are added as they enter the customer stack.
The plugin brings spec-driven development and the team’s standards into Claude Code, Cursor, and Codex. It stands in for a separate upskilling program, so engineers ramp faster, generated code lands closer to standard, and the team ships more without changing how it works.
HIPAA, PCI-DSS, SOC 2, FDA SaMD, WCAG, ISO 27001, FedRAMP, and GLBA. State-level packs are available for California, Texas, and Washington. Internal standards and firm-specific rules sit in a custom regex layer alongside the prepacked set.
The Anthara Gateway sits in front of every AI call. PHI and PII are detected across more than thirty attributes and redacted before any prompt or response leaves the security boundary. Mask and reject modes are configurable per policy.
MCP tool governance at the query level. The PostgreSQL example: SELECT, INSERT, and UPDATE allowed, DELETE blocked org-wide. Every action is logged. Custom agentic workflows are governed by the same policies and guardrails to allow higher levels of autonomy
On-prem or in your VPC. Single-tenant. Air-gapped deployments are supported for the most restrictive environments. Code, prompts, and sensitive data never leave the customer’s security boundary.
Yes. The deployment model keeps PHI inside the customer’s boundary, which is what makes BAA execution tractable. SOC 2 Type II is in progress.
OpenTelemetry-compatible. Every prompt, response, agent action, tool call, and policy decision is recorded with the context needed for an internal review or an external regulator.
A forty-eight hour scan of any repo. No integration. Surfaces structural risk, documentation gaps, sensitive data flow, and security blind spots. The CTO receives a depth-curve narrative. The CISO receives a BAA-ready architecture view and the on-prem evidence.
Anthara is priced per enterprise engagement, not per seat. For pricing, contact us. It is a new AI governance budget line, separate from developer productivity tools like Cursor and separate from compliance posture tools like Vanta or Drata.
A free one-month implementation service is included. Anthara engineers fine-tune the platform to internal workflows, regulatory packs, and coding standards before the team starts using it.
Scanners like Snyk, Sonar, and GitHub Advanced Security verify code after it has been written. Anthara governs while it is being written. Anthara extends scanner coverage rather than replacing it. The same applies for SAST, DAST, and AppSec tools already in the stack. Scanners also add iterations that slow the release cycle. Anthara needs fewer iterations, which means lower cost and faster releases.
Compliance posture tools like Vanta and Drata prepare for audit by tracking controls and evidence. Anthara enforces in real time at the point AI produces work. Posture proves a control exists. Anthara is the control on the AI path.
LLM runtime security protects the model boundary against prompt injection and jailbreaks. Anthara governs what agents and assistants produce inside the codebase, with regulatory packs, org-wide context, and a full audit trail. Different layer, complementary scope.
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