Services
What we build
AI Copilots
Context-aware assistants embedded directly in your product - helping users write, decide, and act faster without leaving their workflow.
Content Engines
Systems that generate on-brand content at scale: product descriptions, marketing copy, email sequences, and reports - with human-in-the-loop controls.
Multimodal AI
Applications that reason across text, images, audio, and documents - enabling use cases like document intelligence and visual search.
Conversational AI
Production-grade chatbots and voice agents with memory, context windows, escalation logic, and deep integration into your data sources.
"Most GenAI projects work in the demo.
Ours work in production."
Common failure modes
- →Hallucinations in critical paths
- →No output validation layer
- →Prompt drift over time
- →Resource usage spirals unexpectedly
- →No feedback loop for improvements
How we prevent them
- →Structured output schemas + type-checking
- →Automatic assertion tests on every output
- →Prompt versioning and regression testing
- →Budget guardrails and per-request usage tracking
- →Eval pipelines that catch degradation early
What you get
- →Reliable outputs users can trust
- →Clear audit trail for every generation
- →Resource predictability from day one
- →A system you can improve over time
- →Compliance-ready logging
Model selection
Choosing the right model
Open-source LLMs
Llama 3, Mistral, Phi-3
Best when
- ✓Full data privacy required
- ✓High-volume workloads
- ✓On-premise deployment
Trade-offs
- –Self-managed infra overhead
- –May need fine-tuning for quality
- –Smaller context windows
OpenAI GPT-4o
Best-in-class reasoning
Best when
- ✓Complex reasoning tasks
- ✓Broad capability needed
- ✓Rapid prototyping
Trade-offs
- –Per-token cost at scale
- –Data leaves your infra
- –Rate limits to plan around
Anthropic Claude
Long context, safe outputs
Best when
- ✓Long documents (200k+ tokens)
- ✓Safety-critical applications
- ✓Nuanced instruction following
Trade-offs
- –Slower for simple tasks
- –Smaller ecosystem
- –Cost comparable to GPT-4
Reliability
Guardrails & evals
Every system we ship includes four layers of protection that run on every inference call.
Output validation
Structured schemas enforce that model outputs conform to expected types and formats. Malformed outputs are retried or flagged - never passed to downstream systems.
Content moderation
Configurable filters for brand safety, toxicity, PII leakage, and regulated content. Applied pre- and post-generation depending on risk level.
Cost monitoring
Per-request token budgets, daily spend limits, and automated alerts. You always know what each feature costs before it hits production.
Drift detection
Evaluation pipelines that run golden test suites on every model update or prompt change. Regressions are caught before they reach users.
Engagement model
From kickoff to launch in 8 weeks
A typical GenAI engagement moves through four clear phases - each with defined outputs and a review gate.
Weeks 1–2
Discovery
- ·Stakeholder interviews
- ·Data & system audit
- ·Use case prioritisation
- ·Scope & success metrics
Weeks 3–4
Prototype
- ·First working build
- ·Prompt engineering
- ·Integration scaffolding
- ·Internal demo + feedback
Weeks 5–6
Hardening
- ·Eval harness built
- ·Guardrails implemented
- ·Load & cost testing
- ·Staging environment
Weeks 7–8
Launch
- ·Production deployment
- ·Monitoring live
- ·Runbook handover
- ·30-day support window
Client results
"We went from a demo to a product our customers use in six weeks. The eval framework alone saved us months of debugging."
Head of Product
B2B SaaS platform, Series B
"StartxLabs didn't just build the AI feature - they built the infrastructure around it that makes it actually trustworthy at scale."
CTO
Enterprise content platform
Impact in production
Tools & models
Our GenAI stack
Common questions
FAQ
How do you prevent hallucinations in production GenAI systems?
We layer structured output schemas, automatic assertion tests, and retry logic on every inference call. For high-stakes outputs, we add a confidence scoring step that routes uncertain responses to human review rather than showing them to end users.
Which LLM should we build on?
It depends on your latency, cost, privacy, and capability requirements. We run a structured model selection exercise in the first week - testing 2–3 candidates on your actual use cases - before making a recommendation. We avoid lock-in by designing for multi-provider fallback from the start.
How do you handle prompt drift as models update?
We maintain a versioned prompt registry with a golden test suite that runs on every model update and deployment. If any regression is detected, the deployment is blocked and you're alerted - regressions never silently reach users.
Can you integrate GenAI into our existing product without a full rebuild?
Yes - most of our engagements are integrations into existing products, not greenfield builds. We design the GenAI layer to fit your existing auth, data model, and API conventions so your team can extend it after we hand over.
What does 'production-grade' actually mean to you?
It means: cost tracking per request, latency monitoring with P95 alerts, output validation with failure modes that degrade gracefully, eval suites that gate every deployment, and runbooks your team can actually follow at 2am.
Case study