AI Enablement

From AI use to AI fluency.

A playbook implemented for your organization. Customized to your standards, your tools, and the regulatory requirements health tech lives under. We leave when it is yours to run.

HealthyAI

90 days idea to production.

Kyruus

Enterprise RAG live in two weeks.

Rolai

Legacy to AI-enabled.

Handing out AI coding licenses creates four new problems.

What breaks when engineering teams adopt AI without an org-aware playbook in place.

Start with a codebase your agents can read.

The context layer that makes everything underneath work.
Install this before the rest of the playbook lands.

Baseline and context

Architecture and decisions

Navigation and retrieval

Codebase too rough for this to land?

Modernization runs first.

What we install.

Guardrails and governance

Cost and control

Shared tooling

Quality gates

Autonomous agents and AI automation

Your Claude ecosystem fine-tuned to your org. Automation that ships work end to end.

How we work.

What every install carries.
01

Your stack.

The gateway, the plugin, the agents, and the standards all run on your side. The artifacts are yours the moment they go in.
02

Your standards.

Your staff engineers co-build the custom skills and standards with us. They become the owners and the teachers for the rest of the team.
03

Autonomy enabled.

We install the infrastructure your team will work with. The fluency that follows is your team’s work, on real tickets in your repo.

Same discipline, every install.

The IP under the playbook is a product. We run on it.

Most consultancies hand you a deck. Ours is grounded in a product we run every day.

Anthara

The conduct layer for enterprise AI in regulated software engineering. Codebase context, tool harnesses, MCP boundaries, quality gates, all of it in a product. Available from day one, without the engagement.

bee

A standalone Claude Code plugin, open-sourced by Incubyte. Bee solves the upskilling side, it gets engineers moving with AI faster. It does not run on Anthara and does not replace the rest of the playbook.

The IP under the playbook is a product. We run on it.

Most consultancies hand you a deck. Ours is grounded in a product we run every day.

Anthara

Anthara is the whole playbook as a product. Codebase context, tool harnesses, MCP boundary, quality gates, live on day one. Plus every capability we add as the product grows.

bee

bee is the open-source Claude Code plugin we built to upskill our own engineers on AI-assisted coding. It codifies our craft, sets a high bar for AI generated code, and solves the upskilling piece of the playbook. We use it daily. It’s free to install.

Ready to get your team to AI fluency?

Tell us about your team and where AI is breaking down today.
Field notes from the engineering team.

Questions engineering leaders
ask before they hire us.

Questions engineering leaders ask before they hire us.

How do you handle PHI and PII?
Nothing leaves your boundary. The gateway and the data-protection layer run inside your HIPAA-compliant infrastructure, prompts are screened before they reach a model, and every AI action lands in an audit log your compliance team can read.
Yes: HIPAA, HITRUST, SOC 2, FDA, GxP. The playbook configures around whatever your stack lives under. We have installed inside Medicare Advantage audit boundaries and in environments that shipped SOC 2 from day one.
They can’t, once the playbook is installed. MCP whitelisting and blacklisting blocks general-purpose assistants from receiving PHI, and org-aware tools take their place.
Acceptance criteria come before the first prompt, behavior evals run in the pipeline, and reviewer ladders sit on high-stakes changes. Generated code merges only after it clears review like any engineer’s code would.
Most installs run 6 to 12 weeks. Because the playbook is productized, there is a defined finish: installed, customized to your standards, handed off.
The install is scoped to an outcome with a defined finish. We give you a pricing range on a 30-minute call.
Get Anthara. It is the same playbook in product form, and your team installs it. We are a call away if you want help later.
We are India-headquartered, with daily overlap across US business hours.
It is an install. Our engineers set up guardrails, shared tooling, quality gates, and autonomous agent harnesses in your codebase, customized to your standards and your regulatory requirements. Then we hand the whole thing to your team. Most installs run 6 to 12 weeks.
AGENTS.md, CLAUDE.md, ADRs, a code-health baseline, the MCP whitelist and blacklist, the eval harness, reviewer ladders, and autonomous agent harnesses. Every deliverable is a named artifact in your repo, not a slide.
Claude, Cursor, Codex, and whatever your team already runs, configured to your standards, your guidelines, and your compliance constraints.
MCP is how your existing tools become AI-aware. We whitelist the safe ones, blacklist the rest, and integrate across your tool chain, from the repo and tickets to data stores and the compliance layer.
Agents that ship work end to end inside the harnesses we install, with nothing merging until it clears your review gates. The harness is the difference between speed and risk.
Yes. The playbook integrates with what you already run rather than replacing it.
Most are not, which is why AI Enablement starts with codebase readiness. If your codebase needs deeper cleanup than that, Modernization runs first.
Whatever your stack and compliance need: closed-source frontier models where reasoning demands it, open models where data or cost does.
We run a codebase walkthrough, capture your standards, inventory your tools, and map your compliance constraints. Engineers are in your repo by day two.
Time to first AI-assisted ship and team-level cycle time show the speed. Review pass rate on agent-generated code shows the standards holding, and your next audit trail shows compliance holding.
Yes. We install alongside in-house teams and hand them the keys.
How do you handle PHI and PII?
Nothing leaves your boundary. The gateway and the data-protection layer run inside your HIPAA-compliant infrastructure, prompts are screened before they reach a model, and every AI action lands in an audit log your compliance team can read.
Yes: HIPAA, HITRUST, SOC 2, FDA, GxP. The playbook configures around whatever your stack lives under. We have installed inside Medicare Advantage audit boundaries and in environments that shipped SOC 2 from day one.
They can’t, once the playbook is installed. MCP whitelisting and blacklisting blocks general-purpose assistants from receiving PHI, and org-aware tools take their place.
Acceptance criteria come before the first prompt, behavior evals run in the pipeline, and reviewer ladders sit on high-stakes changes. Generated code merges only after it clears review like any engineer’s code would.
How long does an Enablement engagement take?
Most installs run 6 to 12 weeks. Because the playbook is productized, there is a defined finish: installed, customized to your standards, handed off.
The install is scoped to an outcome with a defined finish. We give you a pricing range on a 30-minute call.
Get Anthara. It is the same playbook in product form, and your team installs it. We are a call away if you want help later.
We are India-headquartered, with daily overlap across US business hours.
What is AI Enablement at Incubyte?
It is an install. Our engineers set up guardrails, shared tooling, quality gates, and autonomous agent harnesses in your codebase, customized to your standards and your regulatory requirements. Then we hand the whole thing to your team. Most installs run 6 to 12 weeks.
AGENTS.md, CLAUDE.md, ADRs, a code-health baseline, the MCP whitelist and blacklist, the eval harness, reviewer ladders, and autonomous agent harnesses. Every deliverable is a named artifact in your repo, not a slide.
Claude, Cursor, Codex, and whatever your team already runs, configured to your standards, your guidelines, and your compliance constraints.
MCP is how your existing tools become AI-aware. We whitelist the safe ones, blacklist the rest, and integrate across your tool chain, from the repo and tickets to data stores and the compliance layer.
Agents that ship work end to end inside the harnesses we install, with nothing merging until it clears your review gates. The harness is the difference between speed and risk.
Will this work with our existing CI, repo, and ticketing?
Yes. The playbook integrates with what you already run rather than replacing it.
Most are not, which is why AI Enablement starts with codebase readiness. If your codebase needs deeper cleanup than that, Modernization runs first.
Whatever your stack and compliance need: closed-source frontier models where reasoning demands it, open models where data or cost does.
What does the first week look like?
We run a codebase walkthrough, capture your standards, inventory your tools, and map your compliance constraints. Engineers are in your repo by day two.
Time to first AI-assisted ship and team-level cycle time show the speed. Review pass rate on agent-generated code shows the standards holding, and your next audit trail shows compliance holding.
Yes. We install alongside in-house teams and hand them the keys.