TechHala
Enterprise AI solutions

AI for SDLC

Turn business intent into traceable, reviewed, working software.

An AI-driven software development lifecycle: plan, build, verify, and ship with specialized agents — and keep humans in control of every decision. Powered by our HAL-SDLC platform.

Turn business intent into traceable, reviewed, working software."Customer portal with SSO and usage dashboard"planbuildreviewshipauthapiui10 personas · 0 P091/100 · APPROVED

The problem

Code assistants speed up typing. They don't speed up delivery.

Teams adopting AI coding tools see faster snippets but the same bottlenecks: unclear requirements, unreviewed output, inconsistent standards, and no record of why something was built. The result is more code with less confidence.

Our approach

Lifecycle first, code second

  1. 01

    Structured planning

    Business intent becomes a plan with acceptance criteria, architecture notes, and a dependency graph — reviewable before any code exists.

  2. 02

    Orchestrated build

    Specialized agents implement in parallel by phase, grounded in your conventions, documentation, and past decisions.

  3. 03

    Independent verification

    Separate reviewer agents score every deliverable across correctness, quality, security, and tests. Nothing merges on trust alone.

  4. 04

    Continuous learning

    Each cycle captures learnings back into the knowledge layer, so the next plan is better than the last.

Capabilities

Capabilities

HAL-SDLC platform

Our engine for AI-driven planning, execution, and review — deployed inside your environment.

Agent enablement

Skills, rules, and context so agents follow your architecture and standards.

Knowledge layer

Your docs, ADRs, and codebase distilled into a searchable wiki agents actually use.

Quality gates

Multi-persona review, security audit, test generation, and pre-ship polish.

IDE & MCP integration

Works with Cursor and any MCP-compatible tooling your developers already use.

Adoption program

Pilot, measure, and roll out across teams with clear governance.

Outcomes

  • Faster idea-to-PR cycle with fewer review rounds
  • Consistent standards across teams and repositories
  • A complete audit trail for every AI-generated change
  • Developers focused on judgment, not boilerplate

Typical use cases

  • Modernizing a legacy platform module by module
  • Building internal tools and customer portals at speed
  • Scaling engineering output without scaling headcount
  • Regulated environments that require traceability

Have a system to build — or one to keep running?

Tell us about it. We'll come back with a concrete approach, not a slide deck.