ReproAlign

AI-Powered
Product
Engineering.

We design, build, and ship production AI for organisations that measure success in shipped systems and shifted metrics, not slideware.

Currently accepting briefs.
Start a brief See selected work
Engagement arc~12 wk to production
  • Brief
    Day 0
  • Discover
    Wk 2
  • Build
    Wk 6
  • Hand over
    Wk 12
Discovery  2 wk
Build  8–10 wk
Handover  1–2 wk
Accepting new briefs
Currently shipping in three sectors.
Reproductive health · Care & compliance · Mid-market SaaS
Reply
≤ 1 business day
First step
Brief, then call
10K+
Patients served
150+
Facilities deployed
40%
Admin time recovered
98%
On-time delivery
02What we build

A small studio with a deep stack.

Seven disciplines, one delivery team. Every engagement is led by a senior engineer who stays on the project through to handover. We pick the right tools for the job and stay flexible to your team's preferences.

  • 01

    Applied AI engineering

    4–10 weeks

    RAG, agents, copilots, and assistants. Engineered for production with full observability.

  • 02

    Product engineering

    6–12 weeks

    Customer-facing platforms, internal dashboards, and mobile apps that ship and scale.

  • 03

    Data & integrations

    3–6 weeks

    Pipelines, ETL, third-party integrations, and reporting layers built to last.

  • 04

    Infrastructure & DevOps

    2–4 weeks

    Production infrastructure, CI/CD, on-call playbooks, and cost-controlled cloud setups.

  • 05

    Compliance & assurance

    Ongoing

    Security architecture, control mapping, and audit-ready evidence across HIPAA, GDPR, and SOC 2.

  • 06

    AI engineering transformation

    6–12 weeks

    AI-native rollouts, augmented testing, and intelligent CI/CD for engineering teams of any size.

  • 07

    Embedded technical advisory

    Monthly

    Fractional CTO support, architecture reviews, and hiring panels for scaling teams.

Tooling is selected per engagement. We work across the major model providers, cloud platforms, and open-source ecosystems, and brief on the right combination once we understand the scope.
03How we work with your team

AI-native delivery, embedded in your team.

Three phases. One contract. We don't hand off between strategy and engineering. The same team writes the brief and ships the code.

Phase 01
Discover

We sit with your team for two weeks. We map the workflow, identify where AI moves a real metric, and write a brief that doesn't need translating.

Deliverables
  • Architecture audit
  • Risk register
  • Decision brief
Phase 02
Build

Senior engineers ship the system end to end. You see working software each Friday. We write the deployment scripts, not just the prototype.

Deliverables
  • Production system
  • Deployment + observability
  • Runbook
Phase 03
Hand over

Your team runs it after we leave. We pair with your engineers, document the system, and stay on call until the second on-call rotation completes without incident.

Deliverables
  • Handover docs
  • Pairing weeks
  • On-call backup
Note

Most engagements ship to production within twelve weeks. Where we can't commit to a date with confidence, we say so on the discovery call.

04AI engineering transformation

Bring AI-native engineering to the team you already have.

We help engineering organisations move from traditional development to AI-augmented delivery, without rewriting the team or the codebase. Six to twelve week programmes that leave your engineers shipping faster on the stack they already run.

Best for   Teams of 8–80 engineers
Lead time   6–12 weeks
01Pillar

AI-paired development

Roll out Cursor, Claude Code, and Copilot the right way: workspace conventions, prompt patterns, code-ownership rules, and the guardrails to keep velocity gains from becoming review debt.

02Pillar

AI-augmented testing & QA

Automated test generation, mutation testing, and AI-driven regression coverage that scales without growing the QA headcount. Coverage tied to the diff, not the calendar.

03Pillar

Intelligent CI/CD

AI-led code review, pull-request triage, automated release notes, and security scanning baked into the pipeline. The pull request becomes the artefact, not the meeting.

04Pillar

Engineering enablement

Training, internal playbooks, and pairing weeks that turn your existing team into AI-fluent shippers. Knowledge stays inside the company after the engagement closes.

↓Cycle time
Days, not sprints
↑Review throughput
2–4× without headcount
↑Test coverage
Tied to the diff
↓Release lead time
Weekly to daily
Where to start

Most transformations measurably move cycle time within six weeks. We start with a two-week diagnostic to size the opportunity, calibrate the rollout, and write the success metrics, before any tools are wired in.

Talk transformation
05Selected work

Engagements that have shipped.

A representative cross-section. Detailed write-ups available under NDA on request.

Project 01Healthcare · Fertility

Patient platform for a high-volume IVF group.

Replaced four disconnected systems with a single clinician-facing platform. Reduced clinical documentation time by 40% in the first quarter post-launch.

Patients served
10K+
Documentation time saved
40%
Time to production
11 weeks
Project 02Care · Compliance

Learning management system for a UK care-home group.

Consolidated training, attestation, and audit evidence across 150+ facilities. Passed CQC inspection without exceptions in the first cycle after rollout.

Facilities
150+
Certification completion
98%
Audit cycle
CQC-ready
Project 03Operations · Automation

Workflow automation for a mid-market services operator.

Migrated fifteen recurring workflows from manual spreadsheets to monitored automations. Cut manual effort by 70% with no measured uptime regression.

Workflows
15+
Manual effort cut
70%
Uptime
99.9%
Project 04Health · Diagnostics

Standardised semen-analysis assistant aligned to WHO 6E.

Deployed AI-assisted grading across four reference labs. Inter-rater grade variance dropped to ±0.4 against the WHO 6E gold standard.

Standard
WHO 6E
Grade variance
±0.4
Labs deployed
4
06What we measure

The numbers we hold ourselves to.

Self-reported metrics from our own engagement records, compared against publicly cited industry benchmarks where available.

Window   2023–2025
Sample   24 engagements

Time-to-production

Median from contract signature to live system, AI-enabled engagements (2023–2025).

NEXUS11 weeks
Industry26 weeks (industry median)

First-quarter retention of shipped systems

Share of engagements still in production 90 days post-launch, with no major rewrite.

NEXUS96%
Industry61% (analyst benchmark)

On-time milestone delivery

Milestone delivery within agreed window across the last 24 engagements.

NEXUS98%
Industry72% (industry typical)

Defect rate at handover

Production-severity defects per kLOC, measured during the first two weeks post-handover.

NEXUS0.4 / kLOC
Industry1.6 / kLOC (industry typical)
07In their words

What clients say after handover.

They wrote the spec, then ran the deployment. We never re-explained anything. The platform shipped in eleven weeks and is still our system of record.
Founder & Clinic OwnerFertility First IVF · Kochi
Our engineers stopped asking when AI would help. Six weeks in, the workflow felt obvious. The retention metric we cared about moved 3×.
Founder & CEOMid-market SaaS · London
150 facilities, one audit cycle, zero exceptions. The platform replaced four spreadsheets and a contractor. We renewed before the warranty closed.
Owner & Managing DirectorSOS Care Home Group · UK
08Ways to engage

Three ways to work with us.

Pick the engagement at the briefing call. We fix the terms in writing before any work begins.

ADiscovery sprint
2 weeks · fixed fee

Architecture audit, risk register, written technical brief.

Best for
Founders and CTOs deciding whether to build, buy, or rebuild.
Output
Decision-ready brief
BRapid build
8–14 weeks · milestone billed

Design, build, and ship a production system end to end.

Best for
Teams with a known scope and a hard launch date.
Output
Live system + handover
CEmbedded partner
6–12 months · monthly retainer

Embedded engineering capacity with an SLA-backed contract.

Best for
Operators running production AI and needing on-call coverage.
Output
SLA-backed delivery
09Our delivery stack

The tools we ship with.

Production-tested, boring on purpose. We pick technology our clients can hire for and operate after we leave.

Bias   Operability over novelty
Maintained by   Senior engineers
AI runtime
  • OpenAIGPT family
  • AnthropicClaude family
  • vLLMSelf-hosted serving
  • OllamaEdge inference
Application stack
  • TypeScript
  • Next.js
  • React Native
  • Node.js
Data layer
  • Postgres
  • pgvector
  • Redis
  • BigQueryAnalytics
Infrastructure
  • AWS
  • Google Cloud
  • Terraform
  • Docker · GH Actions
Observability
  • OpenTelemetry
  • Datadog
  • Sentry
  • Grafana
Compliance toolchain
  • HIPAA controls
  • GDPR records
  • SOC 2 evidence
  • Audit trails
10Start a brief

Tell us what you're building.

Two paragraphs is enough. We respond within one business day with a recommended next step: a discovery call, a written brief, or a polite decline.

Channel   Direct to founders
Reply   ≤ 1 business day

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