AI-native eng ops in one sprint. Leave a system, not a deck.
DevIntern is the engineering-operations layer you deploy on a client's existing Jira and GitHub in a week. Run a one-sprint pilot, report hours recovered split by PM and engineering, and hand off configs and playbooks the client owns after you walk out. No SaaS in the loop. No proprietary dashboard to maintain. Nothing for their security team to re-approve.
Pilot timeline
one team · one watch rule
Recovered / week
12 eng + 2 PM client baseline
Self-hosted
client infra · client keys
tools migrated or replaced
plugs into the existing stack
Why clients let it in
Self-hosted is what gets this past the security review.
Every other AI eng-ops pitch dies in the security review. DevIntern doesn't, because there is nothing new for the client's perimeter to admit: no SaaS tenant to vet, no second AI contract to negotiate, no telemetry leaving their VPC.
Zero extra cloud bill
Runs on the client's existing laptops, devboxes, or a spare VM. Nothing new for procurement to approve, no SaaS line item to defend at renewal, no per-seat metering bolted onto the engagement.
Their hardware. Their infra. Unchanged.
Native access to internal context
Plugs into the client's private repos, internal databases, MCP servers, secret stores, custom skills, internal docs, and proprietary API specs. A cloud agent literally cannot see any of these. DevIntern reads them the same way an engineer on the network does.
Internal DBs · private repos · MCPs · secret stores · internal docs.
Compliance & data residency
Code, prompts, tickets, and secrets never leave the client's perimeter. Their VPC, their audit log, their retention policy. Security and legal sign because there is nothing new to review, so the data path stays inside the boundary they already approved.
Their VPC. Their audit log. Their AI contract.
Reuses existing AI contracts & keys
Bring whatever Claude, OpenAI, Bedrock, or Azure OpenAI contract the client already negotiated. No new vendor onboarding, no second invoice, no duplicate token spend. The engagement rides their existing AI agreement.
BYO model · BYO keys · their billing relationship.
The pilot playbook
Day 1 to Week 2: install, ship, report.
Products install locally in minutes. Wire GitHub for review handling, enable auto-review on day one, teach PMs and developers the workflows, and start shipping real PRs before the first status meeting.
- Day 1 Checkpoint 1 / 3
Install & connect
- Install on a laptop or shared devbox. Wire to the client's existing tracker and repo.
- Integrate @devintern/code with GitHub: GitHub App auth and a webhook server for automatic PR review handling.
- Turn on auto-review from the start so every diff gets a second pass before humans see it.
- Week 1 Checkpoint 2 / 3
Ship & enable the team
- Tag a few tickets in the client's tracker. Run end-to-end on real work: agent drafts, engineer reviews, PR merges.
- Walk PMs through @devintern/pm: Figma frames, logs, and prompts into tracker-ready specs.
- Walk engineers through @devintern/code: ticket keys, draft PRs, and when to @mention the bot on review threads.
- Week 2+ Checkpoint 3 / 3
Report & hand off
- Report recovered hours split PM / DEV. The steering deck writes itself.
- Document configs on the client's infra. Leave the operator playbook and run-history exports.
- Confirm PM and dev champions can add watch rules, read the logs, and run both tools without calling you.
The arc of the engagement
From a stuck pilot to a self-running capability.
Before engagement
- "We need to migrate to platform X first," with the pilot blocked on a 6-month prerequisite.
- Security review of a new SaaS vendor takes longer than the engagement itself.
- Productivity uplift pitched in adjectives, with no defensible per-role number.
During engagement
- One repo wired at a time, drafting real PRs on the client's existing AI contract. The JQL filter is just how the tool picks up assigned tickets.
- Hours recovered tracked weekly, split PM / DEV; your steering deck writes itself.
- Add more repos as each lever pays back. No big-bang rollout.
After you walk out
- Configs and run history live on their infra, ownable, no black box.
- Their engineers picked up the playbook without calling; capability is internal now.
- The recovered-hours report keeps generating. The credit stays attached to your name.
The handoff
What the client keeps after you walk out.
A consulting engagement is judged on what's still working six months later. DevIntern's handoff is a checklist of concrete artifacts, not a dashboard license tied to your firm.
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Run history on disk
Every feasibility check, draft PR, and auto-review iteration logged locally. Auditable by sprint, by role, by ticket.
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One-page operator playbook
How to add a watch rule, rotate keys, pause a queue, read the run log. Designed so a tech lead can own it on day one.
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Role-segmented ROI report
PM hours and engineering hours, sprint over sprint. The number you presented in steering keeps generating itself after you go.
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Perpetual seat licenses
One-time per-seat purchase, no renewal cycle. The capability stays with the client team even if the engagement ends.
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No vendor dependency on you
Nothing routes through your firm's infra. If the client never calls you again, the system keeps running. That's the point.
Plugs into the stack the client already runs
No new pipelines, no instrumentation work. DevIntern reads Jira tickets and opens PRs against the repos the client already uses.
Safety gates on every run
The same guardrails security would have asked you to add, already there, on by default, documented in the handoff.
- Feasibility check before any branch: ambiguous tickets get a comment, not a guess.
- Auto-review pass on every diff: catches obvious issues before humans see them.
- Per-repo policy: each codebase carries its own config and credentials, no cross-tenant leakage.
- Outcome logs per task on the client's disk for audit and reporting.
The tools you deploy
Two terminal tools. One uninterrupted loop.
@devintern/pm Planning phase
Turns Figma frames, error logs, and rough prompts into codebase-grounded user stories posted straight into the client's Jira board. The PM hours come back first.
@devintern/code Execution phase
Reads a tracker ticket, runs the client's AI agent of choice on the client's machine, opens the draft PR, and answers review comments on the same PR. Same repo, same reviewers, same keys.
Earn 20% on every referred purchase.
Track referrals, signups, and rewards from a personal dashboard built for advisors who recommend tools. DevIntern licenses are one-time purchases, so commission lands at checkout: no MRR-chasing, no monthly reconciliation, no SaaS-style attribution arguments.
Bring back the hours. Leave behind the system.
Add DevIntern to your next eng-ops or AI-native assessment. Pilot one team this sprint, present the recovered-hours report at the next steering meeting, and walk away with the client owning the layer outright.
Self-hosted · BYO model & keys · perpetual licenses · no migration.
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