AI products that ship, not AI demos that fail in production.
LLM-powered apps with evals from day one, prompt versioning treated as code, rate-limit handling that does not page your team at 2am. We have shipped through three LLM provider migrations.
Why most AI products fail their second month.
AI products in 2026 are easy to demo and hard to ship. A working prototype on Claude or GPT takes a weekend. The product that survives the first month of real users takes most of a year. The hard parts are not the prompts. The hard parts are the eval suite that catches regressions when you change a prompt, the retrieval pipeline that retrieves the right document for the right user, the rate-limit handling that degrades gracefully when the provider goes down, and the cost-monitoring that flags before your monthly bill catches up to your runway.
What changes when a Metafic pod is in your repo.
Evals from day one, not after the first regression
A real eval suite with input/output pairs, automated grading where possible, human review where not. Runs on every prompt change. The single biggest correlation we see with AI products that stick.
Prompt versioning treated as code
Prompts live in git, get reviewed in PRs, ship with feature flags. No more "the prompt changed at some point and nobody knows when".
Retrieval pipelines with measurable quality
Embedding choice, chunking strategy, retrieval scoring, re-ranking. Each decision tested against your eval set, not picked from a blog post.
Provider-agnostic SDK layer
You should be able to flip from OpenAI to Anthropic to a local model in a config change. We build the abstraction.
Cost monitoring as a first-class observability concern
Per-user, per-feature, per-prompt token-spend dashboards. So the surprise on the monthly bill is small.
Who is on the pod for this work.
Pods scale up from here for Enterprise engagements.
Has shipped LLM-powered products to production. Has migrated a stack across providers under deadline.
5+ years software engineering, including production AI/ML work or LLM application work specifically.
Builds the eval suite alongside dev. Treats LLM outputs as testable, not as art.
Tuned to help with prompt-engineering reviews and to generate eval test cases from production traces.
The bugs that bite this stack.
No eval set, so every prompt change is a roll of the dice
The single most common failure mode. We address it in week one.
PII in prompts hitting third-party model APIs
GDPR and HIPAA implications. We add redaction at the boundary.
Single-provider lock-in with no migration plan
OpenAI raises prices or has an outage. You have no fallback. We design for portability.
Token-cost surprises from unbounded context
A retrieval pipeline that grew the context from 8K to 80K tokens because nobody noticed. We monitor.
Honest about scope.
We will not take an AI product engagement where the team has not decided what an acceptable output looks like. If you cannot describe "good" precisely, we cannot evaluate it. We will help define that in week one, but it has to be definable.
Common questions.
OpenAI, Anthropic, or open-source models?
Anthropic and OpenAI for almost all production work. Open-source models when cost or data-residency demand it, or for narrow tasks where a smaller fine-tuned model wins. Most teams should not self-host yet.
Should we fine-tune?
Usually no. Better prompts, better retrieval, and better evals get you 80% of the lift for 5% of the cost. Fine-tune when the task is narrow and the volume justifies it.
How do we measure if our AI product is working?
User-level retention, task-completion rate, manual review of a sample. AI-specific metrics (BLEU, ROUGE) almost never map to product success. Track product success.
Vector database choice?
pgvector for almost everyone (you already have Postgres). Pinecone, Weaviate, or Qdrant only when you need specialised features. Most teams over-pick.
Ready to scope it?
A 25-minute call. We will tell you what we would do, what we would not, and whether a pod is the right shape.
Or stay in the loop. One engineering teardown a week.