AI-native software engineering
Where Ideas Meet Implementation
We design, build, and scale secure digital platforms that help businesses move faster and operate smarter.
Strategic partners
Built with trusted partners
What we build
AI systems, and the software around them
Most AI projects die between the demo and production. We build for the second half: the integrations, the evaluation, the failure modes and the running costs.
Applied AI & Autonomous Agents
Agents, retrieval and LLM applications wired into the systems you already run, with the evals, guardrails and observability that make them safe to leave switched on.
AI Strategy & Advisory
Where AI pays for itself, where it does not, and what to build first, scored on value, data readiness and risk.
AI-Native Product Engineering
Full products built by an AI-augmented team (web, mobile and platform) with AI woven into the product, not bolted on after launch.
Intelligent Automation
Judgement-heavy work that rules engines could never touch: reading documents, deciding, escalating to a human when confidence drops.
AI-Accelerated Modernization
We point AI at the codebase nobody wants to open: mapping dependencies, generating the missing tests, then migrating behind a strangler façade.
How we build
Most firms sell AI.
We run on it.
AI-native is not a service line. It is our delivery loop: every engagement runs through the same four stages, with models doing the mechanical work and senior engineers owning every decision that matters.
The result is the part clients actually feel: fewer weeks between a signed spec and a working system, and far less of the rework that usually eats the difference.
A human signs off on everything. AI accelerates the work; named architects own the architecture, the security review and the code that reaches your repository. No unreviewed output ever ships.
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Context engineering
Before anyone writes code, we index your repositories, schemas, tickets and documentation into a retrievable context layer. Models and engineers then work from the same grounded picture of your system instead of guessing at it.
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Specs a machine can execute
Requirements are written as executable specifications: acceptance criteria, edge cases and the eval cases the system must pass. Ambiguity gets resolved with you at spec time, not discovered in UAT.
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Agent-assisted build
Engineers drive coding agents across implementation, test generation, migration and documentation (the repetitive 60%) then review every diff by hand. Boilerplate stops being a budget line.
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Evals in the pipeline
Model behaviour is regression-tested like any other code. Accuracy, hallucination rate, latency and cost per task all carry budgets, and a breach fails the build, not your customers.
Technology Expertise
A frontier AI stack, on top of real engineering
We are model-agnostic by design, routing each task to whichever model wins on accuracy, latency and cost. Underneath sits the unglamorous stack that keeps AI systems running in production.
OpenAI
Anthropic
Gemini
Mistral AI
LangChain
Hugging Face
Pinecone
PyTorch
TensorFlow
MLflow
Weights & Biases
Databricks
Snowflake
dbt
Elasticsearch
React
Vue.js
Angular
Next.js
TypeScript
JavaScript
Tailwind CSS
Svelte
Nuxt.js
Gatsby
Redux
Webpack
Sass
HTML5
CSS3
Bootstrap
Material UI
Storybook
Node.js
Python
Java
Go
Rust
C#
.NET Core
Ruby
Rails
PHP
Laravel
Scala
Spring Boot
Django
FastAPI
Express.js
GraphQL
Elixir
AWS
Azure
Google Cloud
Docker
Kubernetes
Terraform
Ansible
Jenkins
GitHub Actions
GitLab CI
CircleCI
Nginx
Prometheus
Grafana
Linux
Bash
Vagrant
DigitalOcean
PostgreSQL
MongoDB
MySQL
RedisElasticsearch
Oracle
SQL Server
Cassandra
DynamoDB
CouchDB
Neo4j
SQLite
MariaDB
FirestoreTensorFlowPyTorch
Keras
Pandas
NumPy
Scikit-learn
Jupyter
Apache Spark
OpenCV
Kafka
Anaconda
Matplotlib
Airflow
R
Hadoop
Swift
Kotlin
FlutterReact Native
Dart
Android
iOS
Xcode
Android StudioFirebase
Objective-C
Gradle
Capacitor
Jest
Mocha
Selenium
Cypress
Playwright
Pytest
JUnit
Vitest
Cucumber
Postman
Finance & Debt Recovery
Streamlining Debt Recovery Operations through an Intelligent Multi-Channel Platform
Streamlining Debt Recovery Operations through an Intelligent Multi-Channel Platform
Read Case Study
Healthcare & Telemedicine
Connecting Patients and Doctors through a Bilingual Healthcare Platform
Connecting Patients and Doctors through a Bilingual Healthcare Platform
Read Case StudyOur valued clients
Partnering with visionary brands
Testimonials
What Our Clients Say
"Merik engineered a solar-powered IoT water monitoring system that tracks paddy field levels with near-perfect accuracy. Their dual-sensor design and real-time GSM alerts gave our farmers the tools to adopt sustainable irrigation practices at scale."
"Over two years, Merik built a full-scale debt recovery platform handling operations for PayPal, Rogers, and Fairstone. Their work on multi-channel communications and Credit Bureau reporting has been rock-solid from day one."
"Merik delivered a bilingual healthcare platform with doctor, patient, and admin modules, including full RTL Arabic support. The appointment management and payment systems work flawlessly across web and mobile."
"Merik unified our HVAC, energy, lighting, and security systems into one centralized smart building platform. The real-time IoT dashboards and mobile remote control transformed how we manage our facilities."
"Merik's security team conducted a thorough vulnerability assessment across our networks, Oracle EBS, and Active Directory, covering both our head office and regional sites. The remediation plan they provided was comprehensive and actionable."
Responsible AI
The questions your risk team will ask
Shipping AI into a regulated business is mostly a governance problem. Here is where we stand before you have to ask.
Your data is not training data
We work through enterprise and API tiers with training opt-out, and contract for it. Your proprietary data is never used to improve a third-party model.
Grounded, cited, checkable
Retrieval systems answer from your sources and cite them. When confidence is low the system says so or escalates. It does not improvise.
Adversarially tested
Prompt injection, data exfiltration and jailbreak paths are tested before launch, and agent permissions are scoped so a bad output cannot become a bad action.
Documented for audit
Model choices, data flows, eval results and human oversight points are written down: the evidence base regulators and enterprise procurement now expect.
Have a specific compliance regime in mind: HIPAA, GDPR, PCI DSS or the EU AI Act? Talk to us about compliance before you scope the build, not after.
Tell us what you want
the AI to actually do.
Describe the workflow, the data behind it and what “good” would look like. You get back a straight answer on feasibility, the risks worth worrying about, and whether AI is even the right tool for it.
Direct
Tell us about your project
We typically reply within one business day.