AI Development
AI Engineering

AI Development

From research spike to production model — end to end.

Most teams spend six months getting a model to staging. We've done this forty-plus times. We bring the eval harness, the fine-tuning recipes, and the inference scaffolding so you spend your time on the product, not the plumbing.

40+
Models shipped
94%
Avg eval pass rate
6–14 wk
Typical timeline
0
Models abandoned post-launch
Client outcome
60–80% inference cost reduction, without touching output quality.

Measured across similar ai engineering engagements we've shipped.

Get a proposal
StackOpenAIAnthropicMistralvLLMLangChainPineconePostgresTemporal

What we build

01
Model selection & benchmarking

Structured comparisons across candidate models on your actual data — cost, latency, accuracy — before committing to any architecture.

02
Fine-tuning & RLHF

Domain adaptation using your proprietary data. LoRA, full fine-tune, and DPO/RLHF pipelines with reproducible training runs and versioned checkpoints.

03
Eval harnesses

Automated regression suites that catch regressions before deploy. Golden datasets, LLM-as-judge scoring, and human review loops baked in.

04
Inference optimization

Quantization, continuous batching, vLLM/TGI deployment — we cut cost per call by 60–80% without touching quality.

05
Prompt engineering & guardrails

Systematic prompt architecture, few-shot libraries, and content policy enforcement built into every production surface.

06
Observability & drift monitoring

Per-call cost, token usage, latency percentiles, and quality drift dashboards from day one — alerting when the model starts behaving unexpectedly.

How we Deliver

Week 1–2
Discovery & data audit
We audit your data, define success metrics, and build the eval set before writing a line of model code.
Week 2–5
Prototype & benchmark
Two to three candidate approaches benchmarked on real data. You see the numbers before we commit to an architecture.
Week 5–10
Fine-tune & harden
Production fine-tune with eval regression gates at every checkpoint. No regressions ship.
Week 10+
Deploy & monitor
Model served behind a stable API with cost, quality, and latency monitored — and automated alerts on drift.
Evolve Edge team

From Evolve Edge

“We don't ship AI without an eval harness. Not because clients ask — because it's the only way to know the system is actually working in production.”

FAQ

Do we need fine-tuning or will prompting suffice?
Usually prompting first. We benchmark both approaches on your data and choose the best quality-to-cost ratio. Fine-tuning earns its keep when you need sub-300ms latency or domain-specific recall that prompting can't achieve.
What data do we need to provide?
Labeled examples for your target task — as few as 50 can move the needle with modern few-shot fine-tuning. We help you build the labeling pipeline if you don't have one.
Can you work with self-hosted models inside our VPC?
Yes. We've deployed open-weight models (Llama, Mistral, Phi) inside customer VPCs for data-sensitive deployments where data residency is non-negotiable.
How do you prevent quality regressions after launch?
Automated eval suites run on every model update. Any regression above your defined threshold blocks the deploy and alerts the team — before users see it.

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