Agentic AI · SDLC · ISO 27001 · SOC 2 Type 2
A two-week sprint, done by lunchtime. Reviewed by your own engineers.
Krithi turns a ticket into a reviewed pull request, on your infrastructure, in hours.
It reads your legacy code, plans the work, writes and tests the implementation, then audits the diff for security and accessibility before opening the pull request. Every stage stops at a human approval gate, and nothing reaches main without a named sign-off.
No team to run it? Velocity Pods is the managed option: our engineers drive Krithi and own the review gate for you.
ONE AI SPRINT
A human approval gate sits at every stage. The Orchestration Agent routes each task to the right model and keeps Jira in sync from intake through merge.
The problem
Engineering capacity arrives months after you need it
RUNWAY
The hiring math does not work
- Sourcing a senior engineer runs 3 to 6 months before the first meaningful commit.
- Interview loops, competing offers and notice periods happen while burn keeps ticking.
- Ramp on codebase and domain context pushes productivity later still.
THROUGHPUT
The usual fixes rent the same constraint
- Staff augmentation is still bottlenecked by human ramp-up and time zones.
- Legacy cleanup, new features and AI integration all queue behind the same team.
- Adding headcount raises fixed cost against runway you have already committed.
RISK
Unreviewed AI code becomes tomorrow's debt
- Generic coding tools produce output nobody is accountable for.
- Unreviewed merges create technical debt faster than the backlog clears.
- Regulated environments cannot ship code with no audit trail on who approved what.
The gap: senior-grade engineering output at AI speed, with a human accountable for every merge.
The solution
Your AI engineering team. On demand.
Velocity Pods run your backlog through Krithi, our agentic workflow, with a senior engineer from your pod reviewing every change before it ships.
HOW IT WORKS
Krithi is built for both from day one: modernizing what you already have, and standing up what you do not yet have.
WHAT WE COVER
Four subagents. One merged branch.
Built by the lowtouch.ai team. Each subagent owns one stage of the delivery workflow, and every stage stops at a human approval gate. This is not unsupervised AI.
ORCHESTRATION AGENT
Manages all four stages below: routes each task to Claude Code, Codex, Kimi or Gemma by task type, and keeps Jira or your ticketing system in sync from intake through merge.
STAGE 01
HITLRequirements Extraction
- Reads legacy code and customer feedback
- Drafts requirements with traceability to source
- Gate: requirements approved before planning starts
STAGE 02
HITLPlanning
- Plans the sprint and sequences the tickets itself
- Multiple sprints planned and completed in half a day to a day
- Gate: plan approved before any code is written
STAGE 03
HITLCoding
- Builds and writes the implementation against the plan
- Self-reviews the diff against the approved requirement
- Gate: senior engineer reviews before merge
STAGE 04
HITLTesting
- UI, API and regression checks run automatically
- Defects kick back to Coding without human triage
- Gate: sign-off on a passing, reviewed branch
Who does what
Your team files tickets and reviews. Krithi does the rest.
Nobody changes tools or process. Work enters as a ticket, comes back as a pull request with tests and an audit trail, and three named reviewers decide whether it merges.
1 INTAKE
Your team files tickets and reviews.
ACTOR
Product Managers
File feature and change tickets with acceptance criteria.
ACTOR
QA
Raise defects and regression gaps against the same backlog.
SYSTEM
Ticketing
Jira, Azure DevOps or ServiceNow. Krithi reads the queue.
2 KRITHI
krithi_sdlc_agent
01
Plan
Requirements and sprint plan from the ticket
02
Code
Implementation written and self-reviewed
03
Test
UI, API and regression suites run
04
Compliance
Security and accessibility self-audit
05
Open PR
Pull request with tests and evidence attached
3 HUMAN REVIEW GATE
Engineers, QA and auditors decide.
REVIEWER
Engineers
Read the diff, comment on the pull request, approve or send it back. A senior engineer owns the merge.
REVIEWER
QA
Review test results and coverage against the acceptance criteria on the original ticket.
REVIEWER
Auditors
Check the compliance evidence: findings, rule identifiers and who approved what.
4 MERGE AND PROMOTE
Merge to main
Version control, full history and the review record stay in your repository.
QA
Staging
Production
Defects found at any gate go back to Krithi as a ticket. The loop is the same one your team already runs.
Testing
Every ticket ships with its own test suite
Krithi writes and runs functional and performance tests on the UI and the API with Playwright, in your pipeline, against your environments. The suites stay in your repository whether or not Krithi keeps working on the code.
FUNCTIONAL · UI
Playwright browser flows
User journeys generated from the ticket's acceptance criteria and run headless across Chromium, Firefox and WebKit.
FUNCTIONAL · API
Contract and integration
Request and response assertions per endpoint, including auth, error paths and schema drift against the spec.
PERFORMANCE
Latency and load budgets
Page and endpoint timings measured against a baseline, with a threshold that fails the branch when it regresses.
REGRESSION
The suite grows per ticket
Each defect becomes a permanent test. Coverage compounds instead of decaying between releases.
If you own the P&L
Test debt stops being a line item you fund later. Every release carries evidence, so an escaped defect is a finding with a name on it rather than a surprise.
If you own the roadmap
Acceptance criteria are the test. You can see which criteria passed on which build before you approve the ticket, so "done" means the same thing to everyone.
If you own quality
QA stops writing the scaffolding and reviews results instead: traces, screenshots and videos from failed runs, in Playwright's own report format your team already reads.
Defects found in testing kick back to the Coding Agent automatically. A human only reviews a branch that already passes.
Enterprise-grade capabilities built in
From air-gapped inference to a 35-agent domain skill library, Krithi runs on the full depth of the lowtouch.ai platform.
Requirements traceability
Every requirement is drafted from legacy code and customer feedback, and links back to the source it came from.
Sprints planned and delivered in a day
Krithi plans the work and completes multiple sprints of it in half a day to a day, not a two-week cycle.
Self-review before handoff
The Coding Agent checks its own output against the approved requirement before anything reaches a test run.
Automated UI, API and regression tests
Defects kick straight back to Coding. A human reviewer only sees a diff that already passes.
HITL approval gates
Four gates, one per stage, plus a senior engineer signing off on every commit that reaches your main branch.
Model routing per task
Claude Code, Codex, Kimi or Gemma, chosen by the Orchestration Agent rather than fixed at the platform level.
Runs on your infrastructure
No appliance to install. On-prem or air-gapped where the environment requires it, with zero code egress.
Ticketing stays in sync
Jira, Azure DevOps or ServiceNow updated from intake through merge, so status is never reconstructed by hand.
35+ domain agents
A pre-trained skill library covering migration, finance, insurance and scientific application domains.
Compliance self-audit
Krithi audits its own code before a human sees it
Every diff is checked against the standards your auditors already cite, and each finding is mapped to its rule or CWE identifier. The reviewer opens a pull request that has already passed, with the evidence attached.
Engineering best practice
Style, structure and test depth are checked against the standard your repository already declares, not a generic template.
Security
SAST, dependency and secret scanning run on the branch, with every finding carrying its CWE identifier for the audit record.
Accessibility
UI work is audited for contrast, focus order, names and roles, so procurement questions about conformance have an answer.
THE SELF-AUDIT LOOP
1. Agent self-review
The diff is checked against the approved requirement and the repository's own standard.
2. Automated audit
SAST, SCA, secret scanning and accessibility checks run on the branch.
3. Remediate and re-run
Findings go back to the Coding Agent. The loop repeats until the gate passes.
4. Human sign-off
A senior engineer approves a clean branch, with the audit trail attached to the merge.
Measured on real engagements
Numbers below are from the database migration engagement.
CASE STUDY
MSSQL to Postgres, .NET Framework to .NET Core
A specialty insurer moved its core policy platform off MSSQL and .NET Framework. Krithi converted the stored-procedure estate, rewrote the data access layer for .NET Core and generated the regression suite that proved parity. New data pipelines and an API gateway shipped alongside the migration.
- 113 stored procedures converted
- Zero row-count discrepancy at acceptance
- Query latency held within 15% of the pre-migration baseline
ORIGINAL ESTIMATE
9 months
DELIVERED WITH KRITHI
7 weeks
CASE STUDY
Spreadsheet CFO close to an AI-enabled dashboard
A finance team ran its monthly close across linked workbooks, with reconciliations rebuilt by hand each period. Krithi mapped the spreadsheet logic into tested services, then built a CFO dashboard on top of it with variance explanations drafted by an agent and approved by the controller.
- Spreadsheet logic mapped into tested services
- Close reconciliations run on a schedule, not by hand
- Variance commentary drafted by an agent, approved by the controller
ORIGINAL ESTIMATE
12 months
DELIVERED WITH KRITHI
4 weeks
Managed delivery
No team to run it? Velocity Pods do.
Krithi is an agent, not a headcount plan. If you do not have engineers to drive it, a Velocity Pod is the managed service that does: a dedicated team assigned to your backlog, running Krithi on your infrastructure and reviewing every commit before it merges.
A dedicated team, not a licence
Engineers and a delivery lead assigned to your backlog, working as your team, in your tools.
Senior review on every commit
The pod owns the human side of every HITL gate, so accountability for the merge sits with a named engineer.
Scales with workstreams
Start with one product team, add pods as workstreams multiply. No permanent headcount added on your side.
Try it before you commit
The Free Sandbox Evaluation runs Krithi against a real item from your backlog. You cover infrastructure and token costs, nothing else.
Integrated with your existing toolchain
No rip-and-replace. Krithi works against the repositories, pipelines, ticketing and private infrastructure you already run.
GitHub / GitLab
Source and pull requests
Jira / Azure DevOps
Ticketing sync
Jenkins / GitHub Actions
Build and deploy
SonarQube
Static analysis gate
Playwright / Selenium
UI regression
Postman / REST Assured
API contract tests
Claude Code / Codex
Hosted code models
Kimi / Gemma (on-prem)
Air-gapped inference
Confluence
Requirement sources
Entra ID / Okta
Agent authentication
Slack / MS Teams
Reviewer escalation
AWS / Azure / GCP
Private cloud or on-prem
Frequently asked questions
Is Krithi unsupervised AI writing to my main branch?
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No. Each of the four stages stops at a human approval gate, and a senior engineer reviews and signs off on every commit before it merges. The Coding Agent also self-reviews against the approved requirement, and the test stage runs before a human ever looks at the diff.
Does my code leave my infrastructure?
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Not unless you want it to. Krithi deploys on your own stack, on-prem or in a private cloud, and runs fully air-gapped where the environment requires it, with Kimi or Gemma replacing hosted model calls. The platform is SOC 2 Type II and ISO 27001 attested and GDPR-ready.
Which model writes the code?
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It depends on the task. The Orchestration Agent routes each unit of work to Claude Code, Codex, Kimi or Gemma, so a refactor, a migration script and a UI change do not all go to the same model. Air-gapped deployments route only to the on-prem models.
What if we do not have engineers to run it?
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That is what a Velocity Pod is for. It is the managed delivery service around Krithi: a dedicated team plus a lead, assigned to your backlog, owning the human side of every approval gate. You keep the code, the infrastructure and the review record.
How do we evaluate it without a contract?
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Take the Free Sandbox Evaluation. Krithi picks up a live item from your own backlog, not a canned demo, and works it end to end under the same review gate. You cover infrastructure and token costs and nothing else.
Run Krithi against your real backlog this week
Bring one item from your own codebase. You will see reviewed, tested output on your own infrastructure before any contract exists.
Krithi and Velocity Pods are delivered by lowtouch.ai. ISO 27001 certified, SOC 2 Type 2 attested.