AI-native engineering · Softmatic Inc.

Engineering systems that think, automate, and scale.

Softmatic builds AI agents, intelligent software platforms, autonomous infrastructure, and AI-powered engineering workflows for modern businesses.

Start a Project Explore Our Solutions

AI EngineeringCloudDevOpsMLOpsAgentic Systems

orchestration / run-0412running

REQUESTAdd customer invoice search to billing.

AI Orchestrator

Planning

Code

Testing

Security

Cloud

Infra

Observe

GitHub

Cloud

Kubernetes

Terraform

CI/CD

Databases

APIs

OUTCOMEProduction systemwaiting

REQUESTAdd customer invoice search to billing.

AI Orchestrator

PlanningCodeTestingSecurityCloudInfraObserve

GitHubCloudKubernetesTerraformCI/CDDatabasesAPIs

Production systemwaiting

00:00request received · parsing intent

01 / Foundation

Built for production. Designed for real engineering teams.

Every system we deliver is engineered to run under real workloads, inside real security boundaries, with the controls your teams need to operate it.

01Cloud-native architecture
02Secure AI agents
03Production-grade automation
04Human-in-the-loop controls
05Multi-cloud engineering
06Observability by design
07Infrastructure as Code
08Enterprise security

02 / What we build

AI-native systems for modern engineering.

agents / trace · reconcile-vendor-payments● streaming

plannerdecompose request → 3 tasksok
erp_agenttool · erp.query_invoicesok
policyscope finance.readpass
recon_agentmatch records against ledgerok
approvalfinance-ops reviewhold
executorpost adjustmentsqueued

6 spans · 1 human gate01 / 04

03 / Solutions

Solutions for the systems behind your business.

Discuss your use case →

01Enterprise AI AgentsCustom AI assistants and autonomous agents connected securely to enterprise systems.02Multi-Agent AutomationSystems where specialized agents collaborate across complex workflows.03AI-Powered SDLCAutomate engineering workflows from requirement analysis through production deployment.04Infrastructure AutomationIntelligent cloud provisioning, configuration, compliance, remediation, and optimization.05Platform EngineeringInternal developer platforms that simplify cloud infrastructure and accelerate development teams.06Cloud ModernizationDesign, migration, Kubernetes adoption, cloud-native architecture, and multi-cloud engineering.07MLOpsModel pipelines, deployment platforms, monitoring, evaluation, governance, and AI infrastructure.08Enterprise Chatbots & CopilotsContext-aware conversational systems integrated with company knowledge and operational systems.

04 / AI-native SDLC

From requirement to production, with an agent at every stage.

REQUIREMENT“Add customer invoice search.”Scroll to follow the request through the lifecycle, or select a stage.

01 / 12Requirementcaptured

INPUT: “Add customer invoice search.”
AGENT: Orchestrator
TOOL: Jira
ACTION: Captures the request and drafts acceptance criteria.
OUTPUT: Structured requirement

05 / Autonomous infrastructure

Infrastructure that moves at machine speed — with human control.

Describe the environment you need. An infrastructure agent plans it as code, validates it against your policies, waits for approval where you require it, and keeps it optimized after launch.

Natural-language requirement
AI Infrastructure Agent
Terraform / Pulumi
Security policies
Cloud provider
Kubernetes
Observability
Continuous optimization

control-plane / payments-prod● applying

› “Provision a private Kubernetes cluster for the payments service in us-east-1, with network isolation and monitoring.”

Parse requirement into an environment spec.infra-agentrunning
Provision private Kubernetes cluster.terraform · aws_eks_clusterpending
Configure network policies.calico · default-denypending
Validate security posture.opa · 14 policiespending
Approval: platform-leadhuman-in-the-looppending
Deploy workload.kubernetes · payments-apipending
Enable monitoring.opentelemetry · prometheuspending
Optimization complete.rightsizing · 2 replicas releasedpending

eks · us-east-1 · privatepods 0/12

node-1

node-2

node-3

network: pending
security posture: pending
monitoring: pending
optimization: pending

06 / Enterprise agents

Agents built to work inside your business.

Softmatic designs agents that use your tools and data safely. Between the model and your systems sits an orchestration layer that decides what an agent may access, when a person must approve, and records every action.

MODEL

Large language model

Reasoning, planning, and language. Model-agnostic by design.

→ intent→ proposed tool call← governed result

ORCHESTRATION LAYER

Controls between model and systems

Governed tool access (in use)
Human approvals
Memory
Identity (in use)
Security
Observability
Evaluation
Auditability (in use)

ENTERPRISE SYSTEMS

AGENT: svc-agent-ops
TOOL CALL: crm.get_account(acct_2291)
SCOPE: read:accounts
DECISION: allowed
AUDIT: logged · trace attached

07 / How Softmatic works

Four stages, one accountable engineering team.

08 / Engineering principles

The principles behind every system we ship.

Problem before platform

Technology choices follow business problems.

Automation with control

Human approvals remain available at critical decisions.

Security by architecture

Identity, permissions, isolation, auditability, and governance are designed from the beginning.

Production before prototypes

Systems should survive real workloads, failures, scaling, and operational complexity.

Cloud without lock-in

Architecture should preserve portability whenever appropriate.

Observability everywhere

AI actions, infrastructure changes, models, workflows, and costs should remain visible.

09 / Work

Selected solutions.

Representative solutions showing how we approach each problem class. Client engagements will be published here as they are cleared.

REPRESENTATIVE SOLUTION

AI SDLC automation

01 / 06

PROBLEM: Delivery slowed by manual handoffs between ticketing, code review, testing, and release.
ARCHITECTURE: An orchestrator coordinating planning, coding, review, and testing agents over GitHub and CI, with human approval at merge and release.
WHAT WE BUILT: An agent pipeline from ticket to pull request, automated test generation, policy checks, and generated release notes.
BUSINESS OUTCOME: Shorter path from requirement to production, with every agent action reviewable by the team.

10 / Technology ecosystem

Model-agnostic. Cloud-agnostic.

We choose tools for the problem and design for portability. These are the platforms our engineers work with across the stack.

01AI

OpenAI
Anthropic
Gemini
LangGraph
Llama
Vector databases
RAG architectures

02Cloud

AWS
Azure
Google Cloud

03Infrastructure

Terraform
Kubernetes
Docker
Helm
Istio

04Engineering

GitHub
GitLab
Azure DevOps
Jenkins

05Data & AI

Databricks
MLflow
Kafka
PostgreSQL

06Observability

OpenTelemetry
Prometheus
Grafana

11 / Engagement models

Ways to work with Softmatic.

01AI Transformation ProjectBuild a complete production AI system.02Engineering AutomationAutomate existing SDLC, cloud, or operational workflows.03Platform EngineeringDesign and implement cloud and developer platforms.04Agent DevelopmentBuild custom enterprise AI agents.05Architecture & AdvisorySolve difficult cloud, AI, DevOps, MLOps, and platform architecture problems.06Managed Engineering AutomationContinuously operate and improve automation platforms.

What would you automate if your engineering systems could think?

Tell us the workflow, platform, or operational challenge. Softmatic can help design and build the intelligent system behind it.

Start a Conversation Talk to an Engineer

Tell us what you're building.

No sales script. Your first conversation is with an engineer.

01 · We read your brief
02 · An engineer replies with questions
03 · A working session on your architecture