Services
What we do
Every capability, in detail, on one page. Open the ones you care about. They overlap on purpose, because the work does.
How we approach it
Most owner-operated businesses lose hours a day to work that follows a fixed set of rules. Intake, reminders, follow up, routing, data entry between systems that do not talk. We build the software that does it instead, and we build it around your process rather than asking you to change it.
How it runs
Find the repetitive hour
We watch the work and pick the task where automation pays back fastest.
Automate with checkpoints
The system does the work while a person approves what matters, until trust is earned.
Measure the hours back
Before and after time on task, reported plainly.
What you get
- Intake, reminder and follow up automation
- Integrations between systems that do not natively connect
- AI agents for the steps that need judgment, not just rules
- Internal operations tooling
How we approach it
Off the shelf CRMs assume your business looks like everyone else's. When it does not, you end up with unused fields, workarounds in spreadsheets, and nobody trusting the data. We design the schema around your actual relationships and build the interface on top of it.
How it runs
Sit with the current workflow
The spreadsheet, the inbox, the sticky notes: the system is designed around what actually happens.
Model the data, then the screens
Records and relations come first, so the tool survives its second year of growth.
Migrate and train
Existing data moves in cleanly, and the people who use it daily learn on their own records.
What you get
- Database design and data modeling
- CRM platforms built for a specific relationship, not a generic pipeline
- Data pipelines between operational systems
How we approach it
A model is only worth building if someone does something different because of it. We start from the decision, not the dataset. Then we validate properly: backtesting against history, and evaluation on datasets the model has never seen.
How it runs
Find the decision
A model earns its keep only when a prediction changes what someone does next.
Baseline, then model
The simple rule comes first, so the model has something honest to beat.
Deploy where the decision happens
Predictions land in the tool the decision maker already uses.
What you get
- Predictive models tied to a specific operational decision
- Backtesting against historical data
- Evaluation across multiple independent datasets
- Deployment once the model is validated
How we approach it
Jakob has shipped for more than ten web development clients. We build sites your team can update without calling us, that rank, and that do not need rebuilding in two years. Where it helps, we embed an assistant that answers what visitors actually ask instead of burying it in a FAQ.
How it runs
Map the site to the business
Pages earn their place by the question they answer, and load budgets are set before design starts.
Build and instrument
The site ships with analytics wired in from the first commit, not bolted on after launch.
Hand over running
You get the repository, the deploy pipeline, and a maintenance doc a non-developer can follow.
What you get
- Marketing sites and web applications
- Embedded AI chat trained on your material
- Rebuilds and migrations from an existing site
- Secure system architecture and review
How we approach it
Search is splitting in two. Classic SEO still decides what ranks, and answer engine optimization decides whether a model recommends you when someone asks it for a provider. Both need the same foundation, and both are measurable. Paid acquisition sits on top when the numbers support it.
How it runs
Audit visibility everywhere
Google, and the AI answers that increasingly replace it.
Fix the foundations
Structure, speed, and content that crawlers and language models can both read.
Spend where it returns
Paid media tuned by measured cost per outcome, not impressions.
What you get
- Answer engine optimization (AEO) for AI-driven search
- Technical and content SEO
- Paid advertising on Google, Meta and LinkedIn
- Reputation management
How we approach it
Reporting infrastructure so you can see the business without asking anyone to pull a number. Financial modeling underneath it so a decision can be costed before you make it, not explained after.
How it runs
Agree what counts
The metrics that drive decisions, defined once, with their owner.
Build the pipeline
Data flows from source systems into models that update themselves.
Report for action
Dashboards shaped around decisions, not dumps of every number.
What you get
- Analytics and reporting infrastructure
- Dashboards built on a source of truth
- Financial modeling and scenario analysis
How we approach it
A screen that looks good once is easy. A product that stays coherent as it grows needs a system: components, states, spacing and type decided once and reused everywhere. We design and build that system, and we test it against the people who actually have to use it.
How it runs
Audit what users actually do
Where they stall, misclick, or leave. Findings over opinions.
Design the system, not screens
Tokens, components, and states that keep every future screen consistent.
Prove it in build
The system lands in production code, not in a folder of mockups.
What you get
- Interface design for web applications and internal tools
- Component systems your team can extend without a designer
- Usability and accessibility review of an existing product
How we approach it
Slow software costs quietly: visitors leave, rankings slip, staff wait. We profile first, so the work targets what is actually slow rather than what looks suspicious, then we fix it and show you the numbers from both sides of the change.
How it runs
Measure before touching
A baseline of real user timings, so improvement is provable.
Fix the biggest cost first
The slowest query and the heaviest page, in order of user pain.
Report before and after
The same measurements re-run, with the difference in plain numbers.
What you get
- Core Web Vitals and load time optimization
- Database and query performance work
- Caching and infrastructure review
- Before and after measurement on every change
How we approach it
We build iOS apps in Swift when the product needs the platform, and in React Native when it needs to ship on both platforms from one codebase. That includes the part most teams underestimate: review guidelines, provisioning and App Store submission.
How it runs
Prototype the flow that matters
A tappable build of the core loop exists before any app chrome does.
Build native, test on devices
Real hardware and real network conditions, with TestFlight builds from the first week.
Ship through review
App Store listing, review notes, and the first release managed end to end.
What you get
- Native iOS apps in Swift
- Cross platform apps in React Native
- App Store preparation and submission
- Ongoing releases after launch
How we approach it
We audit the way problems actually surface: by attacking the product. Security review of authentication, APIs and data handling, and quality review of the paths real users take, including the ones nobody tested because nobody thought to. You get findings ranked by what they cost you, each with a fix.
How it runs
Try to break it
The same paths an attacker or a careless Friday deploy would take.
Rank what we find
By damage, not by count. Ten trivial findings do not outrank one bad one.
Verify the fixes
Every finding is re-tested after the fix and closed only when it fails to reproduce.
What you get
- Security audit of authentication, APIs and data handling
- QA, UX and accessibility audit of critical user paths
- Bug hunting on a defined scope
- AI and LLM feature review where a product ships one
- Written findings ranked by impact, each with a recommended fix
Not sure which of these you need?
Describe what you need in an email. If the answer is that you do not need us, we will say so.
Or write to either of us directly.
Jakob Werner
jakobw@praxyssolutions.comNihal Padhy
nihalp@praxyssolutions.com