Language Intelligence
Language models under engineering discipline.
Production language systems: grounded generation, context engineering, fine-tuning, evaluation, serving and safety for large language models.
Architectural layers
Problem
What problem does this solve?
- Models hallucinate when they lack grounding; users cannot trust unattributed answers.
- Generic models do not know your domain, your data or your constraints.
- Serving costs, latency and quality trade-offs are rarely engineered deliberately.
Scope
What CAS builds
- Grounded answer systems with ingestion, context strategy, answer-quality metrics and citations.
- Fine-tuning and adaptation pipelines (full, LoRA/PEFT) with regression evaluation.
- Copilots and assistants embedded in products, with context management and memory.
- Serving architecture: caching, batching, routing between model tiers, fallback paths.
Architectures
Architectures that may be used
Each links into ModLens, the CAS architecture explorer, where the structure and trade-offs are diagrammed.
GPT decoder
open in ModLens →
Decoder-only transformers
open in ModLens →
Encoder–decoder systems
open in ModLens →
MoE routing
open in ModLens →
Method
How CAS approaches engineering
- Measure grounding separately from generation — most 'LLM problems' are grounding problems.
- Fine-tune for format and domain behavior; ground for facts.
- Route queries across model tiers by difficulty; reserve frontier capacity for what needs it.
- Safety is layered: input policy, grounded context, output verification, monitoring.
Outcomes
What can result
- Answers grounded in your corpus, with citations users can verify.
- Controlled quality: regressions blocked before release.
- Serving cost engineered to the task mix, not to the worst case.
Delivery
What the process looks like
01 · Use-case audit
Task inventory, quality requirements, data availability, risk profile.
02 · Grounding design
Corpus engineering, grounding architecture, citation policy.
03 · Model strategy
Base selection, adaptation plan, tiering and fallback logic.
04 · Eval harness
Golden sets, adversarial suites, regression gates in CI.
05 · Serve & observe
Serving topology, caching, cost telemetry, quality monitoring.
Preparation
What a client should prepare
- The corpus the system should know, or a plan to build it.
- Examples of good and bad answers for your domain.
- Latency, cost and privacy constraints stated explicitly.
Outcomes depend on data, constraints and integration reality. CAS states assumptions explicitly and reports negative results when evidence demands them.
Discuss llm systems with an engineer.
Bring the problem; we will bring the architecture, the evaluation plan and the honest feasibility read.