RealmTech builds AI systems and web platforms for clients and vendors. You bring the requirement — our roster of engineers takes it from brief to shipped product, and we're unusually good at getting the first version live.
Trusted by product teams, agencies and vendor partners
Whole products, embedded engineers, or a fortnight of research that settles an argument. Same bench, same standards, scoped to whatever you actually need.
Models that ship, not demos that stall.
We design, evaluate and deploy AI systems that survive real traffic — retrieval pipelines, agentic workflows, fine-tuned models and the evaluation harnesses that keep them honest after launch.
Interfaces with the polish of a product company.
Full-stack web applications — dashboards, marketplaces, portals and internal tools — built on a typed, tested foundation your team can keep extending long after we hand over the keys.
Zero to a fundable product in one quarter.
Our sharpest offering. A dedicated pod takes a founder's thesis and returns a working, instrumented product — scoped ruthlessly, built properly, and ready to put in front of users and investors.
A bench of specialists, matched to your requirement.
Clients and vendors bring the requirement; we staff it from a vetted roster of engineers, then wrap them in the delivery discipline of a studio — leads, reviews, reporting and continuity.
Where the uncertain becomes buildable.
As a registered R&D centre, a share of our capacity is reserved for problems without a known solution — prototypes, benchmarks, technical due diligence and feasibility work that de-risks a bet before it becomes a budget.
Infrastructure that stops being a topic of conversation.
Repeatable environments, observable services and deployment pipelines that let your engineers ship on a Friday — with the cost, security and compliance posture already handled.
Most software houses only quote work they've done before. Because research is a formal part of how RealmTech operates, a share of our capacity is reserved for the uncertain: the spike that decides whether a product is possible at all, the benchmark that settles a build-versus-buy argument, the technical diligence that a board actually trusts.
Sometimes the honest verdict is don't build this. We'd rather deliver that in week three than in month nine.
How the R&D Lab worksFeasibility spikes on questions your team can't estimate yet — new model classes, unproven data volumes, undocumented third-party limits.
We compare approaches on your corpus, not on a public leaderboard, and publish the numbers with the methodology attached.
Hypotheses, experiments and outcomes recorded to a standard that supports R&D tax credit claims in most jurisdictions.
Roughly 1 in 5 R&D engagements ends with us recommending against the original plan. Those are the ones clients thank us for.
Active roster
45 availableFrontend Engineers
React · Next.js · TypeScript
Backend Engineers
Node · Python · Go
AI / ML Engineers
LLM systems · MLOps · RAG
DevOps / Platform
AWS · Terraform · K8s
Product Designers
Systems · Prototyping
QA Engineers
Automation · Performance
Specialist roles — embedded, computer vision, Solidity — sourced on request within two weeks.
Clients and vendors come to us with demands that don't fit their bench — a stack they don't hire for, a deadline that arrived early, a client win they weren't sized for. We keep a vetted roster of engineers and match it to the brief, then wrap the pod in the delivery discipline of a studio rather than the churn of a staffing agency.
Most MVPs die of ambition. A dedicated RealmTech pod takes the thesis, cuts it to the single loop that proves or kills it, and returns a real product — instrumented, deployable, and built on an architecture that survives the round it helps you raise.
Weeks 1–2
Ruthless scoping down to one core loop, plus a clickable prototype you can test on real users.
01Weeks 3–6
Auth, data model, core flows and the one feature the whole thesis rests on — built properly, not stubbed.
02Weeks 7–9
Analytics, billing, onboarding and the design pass that makes it look like a company, not a side project.
03Weeks 10–12
Ship to real users, watch the funnels, and spend the remaining sprints on what the data actually says.
04Four engagements that show the range — an AI pipeline under compliance scrutiny, an operations rebuild, a pre-seed MVP and a catalogue at scale.
Meridian Underwriting
Broker submissions arrived as 60-page PDF bundles. We built a retrieval and extraction pipeline that turns each bundle into a structured risk file, with every field traceable to its source page.
73%
less manual data entry
4.5×
faster submission turnaround
Northline Freight
Forty operators coordinating shipments across six spreadsheets. We consolidated it into a real-time control tower with vendor portals, exception alerting and a mobile view for yard staff.
62%
drop in status-chasing calls
18 min
average exception response, from 4 hours
120+
Projects delivered
45
Engineers on roster
9
Countries served
94%
Client retention
A repeatable path we've run over a hundred times. Every stage has an artefact you can read and a date you can hold us to.
Clients and vendors bring a brief — a spec, a napkin sketch, or a problem nobody has scoped yet. We run a structured intake to separate what is known, what is assumed, and what has to be discovered.
We translate the brief into a role matrix and pull the pod from our engineer roster. You review profiles and meet the delivery lead before anyone writes a line of code.
Before committing to a build, the pod proves the risky part. A short spike settles the unknowns — model choice, data volume, third-party limits — and the architecture is written down.
Two-week sprints, demoed live. Every increment lands behind CI, code review and preview environments, so progress is something you can click rather than something you're told about.
Release engineering, load testing, monitoring and a rollback plan. We treat launch as an engineering event with a checklist, not a date on a calendar.
Keep the pod for continuous iteration, or take a clean handover — documentation, walkthroughs and a support window while your in-house team takes the wheel.
We pick boring where boring wins and reach for the frontier only where it earns its keep. Every choice below is one we run in production and can hand over cleanly.
Sectors we've shipped into
Fintech
Ledgers, KYC flows, risk scoring
Healthcare
Clinical tooling, HIPAA-aware pipelines
Logistics
Routing, tracking, vendor portals
E-commerce
Storefronts, catalogue AI, ops tooling
SaaS
Multi-tenant platforms, billing, analytics
Manufacturing
Telemetry, predictive maintenance
EdTech
Assessment engines, adaptive learning
Real Estate
Listing intelligence, CRM integrations
The reasons agencies subcontract to us twice tend not to be the reasons they called the first time.
We slot in as a subcontracted engineering arm for agencies and system integrators — under your brand, inside your process, with SOW-level reporting.
Clean IP assignment, signed NDAs before intake, isolated environments per client and access revoked the day an engagement closes.
Every engagement carries metrics agreed up front — latency, conversion, cycle time — reported weekly against a baseline we capture in week one.
Your ERP, CRM, data warehouse and auth provider are constraints we design around from the start, not surprises discovered during UAT.
"RealmTech sat in our sprint rituals like an in-house team. Six months in I stopped thinking of them as a vendor."
Sarah Chen
VP Engineering, Lumen Financial
"We handed over a requirement doc and had matched profiles in three days. The pod was productive in week one."
Tobias Andersson
Delivery Partner, Nordwerk Consulting
"Their R&D report told us the approach we'd budgeted for wouldn't work — with evidence. That saved us two quarters."
Priya Nair
Chief Product Officer, Arcline Health
If yours isn't here, send it over — we answer questions before we quote.
With a 45-minute intake call. You describe the requirement, we probe the constraints, and within 48 hours you get a written scope with a role matrix, timeline and price band. Nothing is billable until that document is signed.
It means a portion of our capacity is structured around research rather than delivery, and our engagements are documented to that standard. Practically: we can take on problems with no known solution, produce written feasibility verdicts, and our work is typically documented well enough to support R&D tax credit claims in your jurisdiction. We're not tax advisors — we supply the evidence, your accountant makes the claim.
Both, and roughly half our work is white-label delivery for agencies and system integrators. We can operate under your brand, inside your project management tooling, with reporting formatted for your client.
Matched profiles within 72 hours of a signed brief for mainstream stacks; one to two weeks for specialised roles like ML research or embedded systems. You interview everyone before they start.
A two-week R&D spike or a single embedded engineer for a month. Below that, the onboarding cost is not fair to either side, and we'll usually tell you so.
You do, on payment, without exception. IP assignment is in the master agreement, repositories live in your organisation where you prefer, and we hand over infrastructure access at close.