Engineering systems that move business forward.

Build the product.
Automate the work.
Ship with confidence.

AsterickLabs is a senior engineering partner for digital products, quality systems and practical applied AI. We join teams that need the work to actually run in production — then we make it measurable, testable and cheap to own.

  • Senior-only delivery pods
  • Security & privacy by design
  • Outcomes reported weekly
orchestration · run #4182LIVE
INPUT
Inbound requestform · email · API
DECISION
Decision layerclassify → route → guard
SYSTEMS
CRM
DOCS
ERP
OUTPUT
Validated outputpassed 12/12 checks
latency 1.4sconfidence 0.94cost / run $0.011escalations 2

› run --suite regression --env staging

4

Projects delivered end‑to‑end

92%+

Defects prevented before release

6 wks

Median time to first production release

AI‑ready

Systems instrumented for automation

Services

One engineering partner.
Six ways to move faster.

Each service stands alone and composes with the others — the same team, the same standards, one accountable engagement.

01Product EngineeringDiscovery, architecture and delivery for products that must keep working after launch.ReactNodePostgresAWS02Web & Mobile DevelopmentPlatforms, portals, SaaS and native-feeling apps on one shared architecture.Next.jsReact NativeTypeScript03QA & Test AutomationQuality engineering embedded from day one, with suites that survive edge cases.PlaywrightCypressAppiumCI/CD04AI Agents & Workflow AutomationDeterministic flows where rules suffice, guarded agents where judgement is needed.LLM routingQueuesWebhooks05Private / Self-Hosted AIInference inside your perimeter: data sovereignty, predictable cost, full control.VPCVector storeObservability06AI Operations & OptimizationEvaluations, routing, caching and observability that keep live AI honest.EvalsTracingCost control

Delivery

From ambiguity to a working system.

Select a stage to see what it produces. Every stage ends in an artefact you can review, not a status update.

Discover

One to two weeks of interviews, system walkthroughs and data review. We map how the work actually happens today — including the spreadsheet nobody mentions.

  • Current-state map and constraint list
  • Risk register with mitigation owners
  • Opportunity shortlist, sized by effort

SNAPSHOT

12

stakeholder interviews in a typical discovery

Selected work

Selected systems, built for real-world use.

View All Work
AUTOMOTIVE/APPLIED ML

Carmark — a calibrated price model for the used-car market

A public valuation platform for Pakistan, built on 180,975 market listings. A CatBoost quantile model returns a price band rather than a guess, and a conformal calibration layer makes its 90% confidence claim hold up against held-out data.

Applied MLProductDataSEO
5.97%median price error90.39%interval coverageRead case study

Why teams keep us

Engineering rigor at every layer.

Product-minded delivery

We push back on features that won’t earn their maintenance cost, and write down the trade-off we made.

QA embedded from day one

Test strategy lands with the architecture, not after the first outage. Coverage is reported, not claimed.

Automation that survives edge cases

Flows are built around the exceptions: retries, fallbacks, dead-letter paths and a human route out.

Security and privacy by design

Least privilege, data minimisation and a documented boundary for anything that touches a model.

Clear communication, measurable progress

A weekly written update: what shipped, what moved, what we’re worried about. No dashboards-as-theatre.

Long-term engineering partnership

Handover is a deliverable: runbooks, ADRs and a team that can operate the system without us.

What partners say

“AsterickLabs is a reliable engineering partner. Their team understands complex product requirements, delivers high-quality work, and communicates with the clarity and ownership you want from a long-term technology partner.”
MehranCTO, CarmarkRead the Carmark case study

Insights

Notes from work we shipped — each one a decision we had to make on a real system.

View all insights →
Applied ML4 minYour model’s 90% confidence interval is probably 85%Quantile regression produces intervals that are systematically too narrow. Here is how we measured the gap on a live valuation model, and the calibration layer that closed it.Read AI Engineering4 minOAuth 2.1 between a web app and an MCP serverConnecting a product to ChatGPT and Claude means running a real authorization server. What the flow looks like end to end, and why standards compliance beat per-vendor branches.Read Architecture4 minMulti-tenant isolation you can prove in a compliance reviewScoping data by tenant is easy to claim and hard to demonstrate. The two structural decisions that turn “we isolate tenants” into something you can answer with a query.Read

FAQ

Questions we get before the first call.

Something not covered? Ask us directly.

Operational software with real users and real consequences: customer platforms, internal tools, portals, marketplaces, data and reporting products, and the integrations between them. We are strongest where a workflow has to be reliable, auditable and fast to change.

Your next reliable system starts with one conversation.