Model Selection
We pick the model against your benchmark and your unit economics, not against a leaderboard.
Strategy001
Service / AI Engineering
AI Systems Built to Survive Real Traffic
Most AI projects die between the demo and production. We build the part that survives: evaluation, retrieval, guardrails, and the infrastructure underneath.
- Every engagement
- Eval-first
- Model strategy
- Open + closed
- You own it
- Full source
- Team makeup
- Senior only
What is included
Inside AI Engineering
The concrete pieces of work an engagement covers.
Fine-Tuning
Full fine-tunes, LoRA adapters, and preference tuning when the domain shift genuinely justifies it.
Training002
Evaluation Harness
Offline suites, online scoring, and drift alarms wired into CI so regressions never reach users.
Quality003
Retrieval
Hybrid search, reranking, and chunking strategies tuned against your actual corpus.
Grounding004
Agent Runtimes
Tool-use, planning, and recovery loops with approval gates on anything that writes.
Autonomy005
Guardrails
Policy enforcement, prompt-injection defence, and red-teaming as a standing practice.
Safety006
Unique approach
Why Teams Bring Us In
Not all AI delivery is the same. The difference shows up the week after launch.
With DarCode
- Benchmark defined before any code is written
- Retrieval tuned against your real corpus
- Inference cost modelled before build
- Regression gates in CI on every change
- Full source and infrastructure handed over
- Senior engineers from scoping to ship
Typical alternative
- Success measured by a demo that went well
- Default chunking, default embeddings, hope
- Cost discovered on the first invoice
- Manual spot-checks when something feels off
- Locked to a platform or an agency retainer
- Sold by principals, built by juniors
How it runs
From Kickoff To Handover
The same sequence every time, compressed or extended to fit the engagement.
- 01
Scope
We define the benchmark and the failure modes that actually matter to you.
- 02
Baseline
Cheapest viable approach first, measured, so we know what complexity buys.
- 03
Build
Retrieval, prompts, tools, and guardrails developed against the eval suite.
- 04
Harden
Load, cost, and adversarial testing before anyone outside the team sees it.
- 05
Handover
Source, infrastructure-as-code, runbooks, and a working CI pipeline.
Tooling
What We Reach For
Defaults, not dogma. The stack follows the problem.
- Anthropic
- OpenAI
- Gemini
- Mistral
- Hugging Face
- PyTorch
- LangChain
- Qdrant
FAQ
Frequently Asked Questions
Questions we get asked about ai engineering.
Not always. If you have a corpus we tune against it. If you do not, we start with a public or synthetic baseline and design the data collection alongside the build.
Whichever wins the eval at an acceptable cost. In practice that means frontier models where reasoning depth matters, and open-weights models where inference cost, latency, or data residency dominate.
A benchmark we agree on before the build starts, scored the same way every run, with the failure modes you care about weighted explicitly.
You own everything. We offer a support window and can stay on retainer, but nothing in the build requires our continued involvement.
Yes. A large share of our work is embedding with an in-house team, setting the engineering standard, and handing the system over.
Get started
Let's Build It, Together
Tell us what you are trying to ship. We will tell you the three shortest paths to it, and which one we would actually take.