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Software Development

Systems Engineered for
Production, Not Prototypes.

We build the backend infrastructure, APIs, data layers, and cloud systems that power serious products. Every line of code is written for reliability under load, maintainability over years, and security from day one.

Discuss Your Project See What We Build
Backend Systems REST & GraphQL APIs Cloud Infrastructure Microservices Database Architecture DevOps & CI/CD
02

What Custom Software
Actually Means

Not templates. Not boilerplate. Software designed around how your business operates.

Most organizations eventually outgrow the tools they started with. Spreadsheets become unmanageable. Third-party platforms impose limitations that cost more to work around than to replace. Internal processes that should take seconds require manual intervention across multiple systems.

Custom software development addresses this by building systems that map directly to your operations — your data structures, your business rules, your workflows. Instead of reshaping your processes to fit someone else's software, the software adapts to you.

At Prisma Akatsuki, we approach software development as an engineering discipline. We start by understanding the problem domain thoroughly before writing a single line of code. We design data models that reflect real-world relationships. We architect systems that can handle ten times their current load without a rewrite. And we write code that your team — or another team — can maintain and extend without needing us in the room.

This is the difference between software that works in a demo and software that works at three in the morning when your system is processing ten thousand concurrent transactions.

Common Triggers for Custom Development

  • Your current tools cannot integrate with each other without manual work
  • Off-the-shelf software forces you to pay for features you do not use
  • Your data lives in multiple disconnected systems
  • You need a system that enforces your specific business logic
  • Performance requirements exceed what SaaS platforms can deliver
  • Regulatory or compliance requirements demand control over your data infrastructure
03

What We
Engineer

Twelve core capabilities. Each one grounded in real production experience.

01

Backend Development

Server-side application logic, business rule engines, and service layers that form the operational core of your product. We write backends that handle complex workflows without becoming fragile or opaque.

Node.jsPythonTypeScript
02

API Development

REST and GraphQL APIs designed for consistency, versioning, and clear documentation. We build APIs that third-party developers, internal teams, and mobile applications can consume without friction.

RESTGraphQLOpenAPI
03

Cloud Infrastructure

Infrastructure provisioning, networking, and service configuration on AWS, Azure, or GCP. We design cloud architectures that balance cost, performance, and operational simplicity — avoiding over-engineering as carefully as under-engineering.

AWSAzureGCP
04

Microservices Architecture

Decomposing monolithic systems into independently deployable services when — and only when — the complexity justifies it. We handle service boundaries, inter-service communication, data consistency, and distributed tracing.

DockerKubernetesgRPC
05

Database Architecture

Schema design, query optimization, indexing strategy, and data migration planning. We work across relational and document databases, choosing the right storage engine for each access pattern rather than forcing everything into a single model.

PostgreSQLMongoDBRedis
06

System Architecture

High-level technical design — how components interact, where boundaries exist, what scales independently, and what fails gracefully. We produce architecture documentation that serves as a reliable map for current and future development.

C4 DiagramsADRs
07

Authentication & Authorization

Identity systems, role-based access control, OAuth 2.0 flows, JWT management, and multi-tenant permission models. Authentication is typically where security vulnerabilities originate, so we treat it with appropriate rigor.

OAuth 2.0JWTRBAC
08

Third-Party Integrations

Connecting your system with payment gateways, shipping providers, CRMs, ERPs, communication platforms, and any service with an API. We build integration layers that isolate external dependencies so vendor changes do not cascade through your system.

StripeTwilioWebhooks
09

Performance Optimization

Identifying and resolving bottlenecks through profiling, query analysis, caching strategy, connection pooling, and load testing. We focus on the specific constraints of your system rather than applying generic optimization checklists.

ProfilingCachingLoad Testing
10

Testing & Quality Engineering

Automated test suites at unit, integration, and end-to-end levels. We design test strategies that provide meaningful coverage on critical paths without slowing development velocity through excessive test maintenance.

JestPytestCypress
11

DevOps & CI/CD

Automated build, test, and deployment pipelines. Infrastructure as code. Environment parity between development, staging, and production. We set up systems where deploying a new release is routine, not an event.

GitHub ActionsDockerTerraform
12

Monitoring & Observability

Logging, metrics, alerting, and distributed tracing that give your team clear visibility into system health. We configure monitoring that surfaces real problems rather than generating noise that gets ignored.

GrafanaPrometheusELK
04

How We
Build Software

A methodical process that reduces risk at every stage.

01

Discovery & Requirements

We map your business processes, identify data flows, define user roles, and document functional and non-functional requirements. This phase produces a clear scope document that both sides agree on before any code is written.

02

System Architecture

We design the technical architecture — service boundaries, data models, API contracts, infrastructure topology, and integration patterns. Architecture decisions are documented with rationale so future developers understand the "why" behind each choice.

03

Foundation Sprint

We build the core infrastructure: database schemas, authentication systems, CI/CD pipelines, and the primary service scaffolding. This creates a stable base that all subsequent development builds on.

04

Iterative Development

Features are built in two-week sprints with regular demos and feedback cycles. Each sprint delivers working, tested functionality that can be deployed independently. Priorities are adjusted based on real feedback rather than fixed twelve-month roadmaps.

05

Quality Assurance

Every feature passes through automated testing, code review, and manual QA before merging. We run load tests on performance-critical paths and security reviews on authentication and authorization flows.

06

Deployment & Handoff

We deploy to production using automated pipelines with rollback capabilities. Documentation covers system architecture, API references, deployment procedures, and operational runbooks. Your team receives everything needed to maintain and extend the system.

07

Monitoring & Stabilization

Post-launch, we monitor system behavior under real traffic, tune performance, and address edge cases that only surface in production. This stabilization period typically runs two to four weeks after initial deployment.

08

Ongoing Partnership

For most clients, the initial build is the beginning. We provide ongoing maintenance, feature development, infrastructure optimization, and technical guidance as the system and business evolve.

05

Representative
Case Studies

These examples illustrate typical engagements. Details have been generalized to protect client confidentiality.

Example Project — Financial Infrastructure

Real-Time Transaction Processing Engine

Industry Fintech
Duration 14 Weeks
Type Backend System

Challenge

A fintech startup needed to replace a batch-processing payment reconciliation system with a real-time engine. The existing system processed transactions in six-hour cycles, creating a lag that prevented customers from seeing accurate account balances. The system also lacked proper audit logging, which was becoming a compliance concern as the company scaled.

Strategy & Execution

We designed an event-driven architecture using message queues to process transactions as they occurred. The system maintained an append-only event log for complete audit traceability. We implemented idempotency safeguards to prevent duplicate processing during network failures, and built a reconciliation dashboard that gave the operations team real-time visibility into processing status.

Node.jsTypeScriptPostgreSQLRedisRabbitMQAWS

Key Decision

We chose an event-sourcing pattern over traditional CRUD for the transaction layer. This added initial complexity but provided complete auditability and the ability to reconstruct system state at any point in time — critical for a financial product where transaction integrity is non-negotiable.

Outcome

Representative outcome: transaction processing latency reduced from hours to under two seconds. The audit log satisfied regulatory review requirements. The system handled a hypothetical 15x traffic increase during load testing without degradation.

Representative Case Study — Enterprise

Multi-Tenant Inventory Management Platform

Industry Logistics / Supply Chain
Duration 20 Weeks
Type Enterprise Platform

Challenge

A logistics company operating across multiple warehouses managed inventory through a combination of spreadsheets and a legacy desktop application that could not support concurrent users. Stock discrepancies between warehouses averaged 12%, leading to fulfillment errors and strained client relationships. Each warehouse essentially operated as an isolated system.

Strategy & Execution

We built a multi-tenant web-based platform with real-time inventory synchronization across all locations. The system included barcode scanning integration, automated reorder triggers, role-based access for warehouse staff and management, and a reporting engine that provided cross-location visibility. We migrated historical data from the legacy system over a three-week parallel-run period.

PythonDjangoPostgreSQLRedisDockerWebSockets

Challenges Encountered

Data migration was more complex than anticipated. The legacy system stored product SKUs inconsistently across warehouses, with duplicate entries and conflicting categorizations. We built a data normalization pipeline that identified and resolved approximately 3,400 duplicate records during migration. This added two weeks to the timeline but prevented data integrity issues post-launch.

Outcome

Representative outcome: hypothetical reduction in stock discrepancies from 12% to under 2%. Fulfillment accuracy improved substantially. The reorder automation reduced manual procurement workflows by an estimated 60%.

Sample Engagement — API Infrastructure

Public API Platform for a Data Provider

Industry Data Services
Duration 10 Weeks
Type API Platform

Challenge

A data analytics company needed to expose their proprietary datasets through a public API that third-party developers could integrate with. The API needed to handle rate limiting, API key management, usage metering for billing, comprehensive documentation, and versioning — all while maintaining sub-200ms response times for most queries against datasets containing millions of records.

Strategy & Execution

We built a RESTful API gateway with automatic OpenAPI documentation generation, a developer portal with interactive API exploration, tiered rate limiting based on subscription plans, and a usage analytics dashboard. Query performance was optimized through materialized views, intelligent caching layers, and connection pooling.

Node.jsTypeScriptPostgreSQLRedisNginxDockerSwagger

Lessons Learned

Initial API design included overly flexible query parameters that allowed expensive full-table scans. After load testing revealed timeout issues at scale, we restructured the query API to require indexed field filters and moved complex analytical queries to an async job system with webhook delivery. This trade-off sacrificed some query flexibility but made the API reliably fast for the use cases that actually mattered to developers.

Outcome

Representative outcome: the API platform launched with comprehensive documentation that reduced developer onboarding time. The system maintained p95 response times under 180ms across standard query patterns. Rate limiting and usage metering enabled a tiered pricing model that aligned infrastructure costs with revenue.

06

Technology
We Work With

We choose tools based on the problem, not habit. Here is what we reach for most often and why.

Languages & Runtimes

Node.js / TypeScript — Our primary choice for API servers, real-time services, and event-driven systems where the ecosystem and developer velocity matter
Python — Data-intensive applications, automation scripts, ML integration, and backend services where library support is a deciding factor
Go — High-concurrency services where raw performance and minimal resource consumption are priorities

Databases & Data Stores

PostgreSQL — Our default relational database for transactional workloads, complex queries, and applications requiring strong data integrity
MongoDB — Document-oriented storage for applications with flexible schemas or hierarchical data structures
Redis — Caching, session management, rate limiting, and real-time pub/sub messaging

Cloud & Infrastructure

AWS — EC2, RDS, S3, Lambda, SQS, CloudFront — our most frequently used cloud platform for production deployments
Azure — Enterprise integrations, Active Directory environments, and clients with existing Microsoft infrastructure
Docker & Kubernetes — Containerization and orchestration for reproducible deployments and horizontal scaling

DevOps & Tooling

GitHub Actions / GitLab CI — Automated pipelines for building, testing, and deploying on every commit
Terraform — Infrastructure as code for reproducible, version-controlled cloud environments
Grafana / Prometheus — Metrics collection, dashboarding, and alerting for production observability
07

Who This
Service Is For

Funded Startups

You have validated your idea and need a technical team to build the production system. You need it built correctly the first time because you cannot afford a rewrite in twelve months.

SaaS Companies

You are building or scaling a software product and need backend engineering capacity, infrastructure expertise, or help refactoring systems that have outgrown their initial architecture.

Established Businesses

You need to replace spreadsheets, legacy desktop applications, or disconnected SaaS tools with a unified custom system that maps to your specific operations.

Product Teams Needing Backend Support

Your in-house team is strong on frontend or design but needs backend and infrastructure engineering to complement their work.

When This May Not Be the Right Fit

If your primary need is a marketing website, a WordPress blog, or a simple landing page, custom software development is likely more than you need. We would recommend our web development services instead.

If you are looking for a team to maintain code without understanding or improving it — just keeping the lights on with minimal investment — we are probably not the right partner. Our strength is in building and improving systems, not in passive maintenance.

If you need a single freelance developer for a small, well-scoped task, engaging a full service team may not be cost-effective for your situation.

08

Problems
We Solve

"Our application is slow and we do not know why."

We profile your system to identify the actual bottlenecks — whether that is unoptimized database queries, missing indexes, insufficient caching, memory leaks, or network latency issues. We then address the root causes rather than adding hardware to mask the symptoms.

"Our backend cannot handle our current growth."

We audit your architecture, identify scaling constraints, and implement solutions — whether that means adding caching layers, optimizing database access patterns, introducing message queues for async processing, or restructuring services for horizontal scaling.

"We have technical debt that slows down every new feature."

We conduct a codebase assessment to quantify the debt, prioritize what to address first based on impact, and incrementally refactor the most problematic areas while continuing to ship new features. We do not stop the business to rewrite everything.

"Our data lives in five different systems that do not talk to each other."

We design integration architectures that connect your disparate systems — whether through direct API integrations, an event bus, or a centralized data layer — so your team works with consistent, synchronized information.

"We need to build something custom but do not have the in-house engineering team."

We function as your engineering team for the duration of the project. We handle architecture, development, testing, deployment, and documentation — delivering a system your future hires can maintain and extend.

"We inherited a codebase and do not understand how it works."

We conduct code audits that produce architecture documentation, dependency maps, risk assessments, and prioritized recommendations. We give you a clear picture of what you have, what works, what does not, and what to do about it.

09

Frequently Asked
Questions

Custom software costs depend on system complexity, integration requirements, infrastructure needs, and timeline. A focused API service or backend module may take four to eight weeks, while a full enterprise platform can span several months. We scope every project individually after a discovery session and provide transparent estimates before any work begins. We do not quote fixed prices without understanding the problem first — any team that does is guessing.
Yes. A significant portion of our work involves inheriting, auditing, and improving existing systems. We start with a codebase assessment to understand the current architecture, technical debt, and areas for improvement before proposing a path forward. We have worked with codebases ranging from well-structured monoliths to tangled legacy systems with minimal documentation.
Our core stack includes Node.js, Python, and TypeScript for backend systems. We work with PostgreSQL, MongoDB, and Redis for data layers, and deploy on AWS, Azure, and GCP using Docker and Kubernetes. We choose technologies based on the specific requirements of each project — runtime performance needs, ecosystem maturity, team familiarity, and long-term maintenance considerations — rather than defaulting to a single stack for everything.
Yes. We offer ongoing support agreements covering monitoring, incident response, performance optimization, security patching, dependency updates, and continued feature development. Many of our client relationships extend well beyond the initial build because software that matters to your business requires ongoing attention.
Security is integrated into our development process from the start, not bolted on at the end. We implement proper authentication and authorization patterns, encrypt data in transit and at rest, follow OWASP guidelines, conduct security-focused code reviews, and set up monitoring for suspicious activity. For applications handling sensitive data, we coordinate with third-party security auditors for independent verification.
Off-the-shelf software solves general problems for a broad audience — which means it includes features you do not need and lacks features you do. Custom software is built around your specific operations, data flows, and business rules. It eliminates the compromises inherent in generic tools, integrates with your existing systems on your terms, and scales according to your actual growth patterns rather than a vendor's pricing tiers. The trade-off is higher upfront investment for significantly better long-term fit.
Yes. We architect and build multi-tenant SaaS platforms including user management, subscription billing integration, usage metering, admin dashboards, API layers, and the underlying infrastructure. SaaS platforms have specific architectural requirements — tenant isolation, scalable data models, subscription lifecycle management — that we have direct experience designing for.
Through code reviews on every merge request, automated testing at multiple levels (unit, integration, end-to-end), continuous integration pipelines that block deployment on test failure, static analysis tools for code style and common error patterns, and adherence to established architectural patterns. We maintain comprehensive test coverage on critical business logic paths and performance-sensitive operations.
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