OWASOL

What We Do

Software Built for the Way Work Happens Now

Nine years of shipping web and back-end platforms, now pointed at a harder question: which parts of your business should a machine be handling, and which should stay exactly as they are. We build both sides of that answer — and we are straight with you about where the line falls.

01

AI & Intelligent Automation

The work your team repeats every day is the work a model should be doing. We find it, automate it, and prove the saving.

AI earns its place when it removes hours, not when it appears in a press release. We start from the workflow costing you most, ship the narrowest thing that fixes it, measure the result, and widen from there.

  • Assistants and copilots grounded in your own content
  • Retrieval over internal knowledge bases (RAG)
  • Document intake — extraction, classification, summarising
  • Agentic workflows that act across the tools you already run
  • Model selection, prompt design, evaluation and guardrails
  • Token and inference cost control from day one
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02

AI-Native Product Engineering

Products built so intelligence is part of the architecture — not a feature retrofitted two years later.

Web and mobile platforms designed from the start to carry AI: streaming responses, semantic search, event pipelines and clean data boundaries. The same engineering discipline as any product we ship, with the seams already in place.

  • Web platforms, portals and operational dashboards
  • Cross-platform mobile applications
  • Real-time and streaming interfaces
  • Semantic search and vector-backed data layers
  • Event-driven, queue-backed architecture
  • Progressive web apps and offline-first behaviour
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03

Experience Design

Interfaces people trust. That was always the goal; it matters far more now a machine is answering.

Research, prototypes and design systems, plus the newer problems: how a product shows its reasoning, admits uncertainty, and keeps the person in control. Decisions get tested before they get expensive.

  • User research and journey mapping
  • Conversational and prompt-driven interfaces
  • Designing for uncertainty — citations, confidence, undo
  • Design systems built for reuse
  • Complex dashboards and data-heavy screens
  • Accessibility built in, not bolted on
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04

APIs, Integrations & Data Foundations

AI is only as good as the data it can reach. This is the plumbing that decides whether it works.

Documented, versioned APIs and the pipelines behind them. Most AI projects stall on access to clean, current data — so we treat integration and data quality as the first deliverable, not an afterthought.

  • REST and GraphQL API design
  • Third-party, payment and identity integration
  • Data pipelines, synchronisation and warehousing
  • Embeddings and vector store management
  • Authentication, roles, rate limiting, observability
  • Legacy system modernisation
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05

AI Readiness & Technology Consulting

An outside read on where AI will pay, where it will not, and what has to be true first.

We audit what you run today, map where it breaks under growth or automation, and hand back a costed plan you can act on with or without us. Often the most valuable thing we deliver is a clear no.

  • AI opportunity mapping and business case
  • Architecture, security and code audits
  • Data readiness assessment
  • Build-versus-buy and model selection advice
  • Cloud cost and performance review
  • Team augmentation and CTO advisory
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06

Managed Support & Continuous Improvement

Software rots when nobody is watching, and AI systems drift faster than the rest of it.

Monitoring, patching and iteration under agreed response times. For AI features that includes tracking output quality over time, because a model that was accurate at launch will not stay that way unattended.

  • Proactive monitoring and alerting
  • Output quality and drift monitoring for AI features
  • Security patching and dependency upkeep
  • Fixes under agreed response times
  • Backups and recovery drills
  • Ongoing feature development
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How We Work

Three Habits That Decide Whether AI Work Lands

Most AI projects do not fail on the model. They fail on choosing the wrong problem, and on nobody checking afterwards whether it actually worked.

01

Start where it hurts

We do not open with technology. We open with the process costing you the most hours, because that is where a return is easiest to prove and hardest to argue with.

02

Ship small, measure, widen

Short cycles with a working build at the end of each one. For AI work that means evaluation sets and real numbers — not a demo that impressed everyone once and was never tested again.

03

Honest about the limits

We will tell you when a model is the wrong answer and ordinary software is the right one. That advice has saved clients more money than the features we sold them.

Our Toolkit

The Stack We Build On

Chosen per project, based on what you need to run and maintain in three years — not on what launched last month.

AI & LLMs

OpenAI · Anthropic Claude · open-weight models · LangChain · MCP · RAG & embeddings · Deepgram

Front-end

TypeScript · Vue · Nuxt · Next.js · Angular · React · Tailwind CSS

Back-end

Node.js · NestJS · Express · PHP · Laravel · Python

Mobile

React Native · Flutter · Progressive Web Apps

Data & Storage

PostgreSQL · pgvector · Neon · Supabase · Firebase · MySQL · MongoDB · Redis

Cloud & Operations

AWS · Docker · CI/CD pipelines · Nginx · Langfuse · n8n

Not sure which part of this you need?

That is a normal place to start. Tell us what the work looks like today and we will come back within one business day with honest questions and a realistic view of scope.

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