FIG 1. LLM APPLICATION STACK
[USER QUERY]|+----v---------+| Prompt || Template |+----+---------+|+----v---------+ +------------+| Context |--->| Token || Assembly | | Budget |+----+---------+ +------------+|+----v---------+| LLM API || (w/ retry) |+----+---------+|+----v---------+| Output || Parser |+----+---------+|+----v---------+| Validation || & Fallback |+--------------+
THE LLM STACK
Building with LLMs requires more than API calls. We architect complete systems with prompt management, context windows, output parsing, and fallback strategies — turning unreliable model outputs into dependable product features.
Our LLM stack includes structured output parsing with Zod schemas, automatic retry with exponential backoff, token budget management, and multi-provider failover. Every prompt is version-controlled and evaluated against test suites before deployment.
An LLM demo takes a weekend. An LLM product takes engineering discipline.
- Multi-provider support, with automatic failover between OpenAI, Anthropic, and open-source models.
- Structured output parsing, enforcing schema validation on every model response.
- Token budget management, optimizing context window usage and controlling costs.
FIG 2. RAG PIPELINE
[QUERY] ----+|+-----v------+| Embedding |+-----+------+|+--------+--------+| |+--v------+ +------v--+| Vector | | Keyword || Search | | Search |+--+------+ +------+--+| |+--------+--------+|+-----v------+| Reranker |+-----+------+|+-----v------+| Context || Assembly |+-----+------+|+-----v------+| LLM Gen |+------------+
RAG ARCHITECTURE
Retrieval-augmented generation that actually retrieves the right context. We build RAG pipelines with hybrid search, reranking, and chunk optimization — going far beyond naive vector similarity.
- Hybrid search, combining dense vector embeddings with sparse keyword matching for better recall.
- Intelligent chunking, using document structure awareness to preserve semantic boundaries.
- Reranking pipeline, scoring retrieved chunks by relevance before feeding them to the LLM.
FIG 3. FINE-TUNING LOOP
[BASE MODEL] [DOMAIN DATA]| || +-----v------+| | Curate & || | Format || +-----+------+| |+----v----------------v----+| LoRA / QLoRA || Fine-Tuning |+----+---------------------+|+----v---------+| Evaluate || Test Suite |+----+---------+|pass? --+-- fail?| |+----v---+ +--v--------+| Deploy | | Iterate |+--------+ | Dataset |+-----------+
FINE-TUNING & EVAL
Generic models give generic answers. Fine-tuning gives you a competitive edge. We fine-tune foundation models on your domain data, then rigorously evaluate them against curated test sets to ensure they outperform the base model where it matters.
Our evaluation framework goes beyond simple accuracy metrics. We test for hallucination rates, instruction following, edge case handling, and adversarial robustness. Every model variant is benchmarked before it reaches production.
- LoRA and QLoRA fine-tuning, for parameter-efficient training on domain-specific data.
- Evaluation frameworks, with automated test suites measuring accuracy, safety, and relevance.
- Dataset curation, building high-quality training sets from your existing knowledge base.
FIG 4. SAFETY LAYER
[USER INPUT]|+----v---------+| Injection | <-- Block| Detection |+----+---------+|+----v---------+| PII | <-- Redact| Scanner |+----+---------+|+----v---------+| Topic | <-- Filter| Classifier |+----+---------+|+----v---------+| LLM || Generation |+----+---------+|+----v---------+| Output | <-- Validate| Filter |+--------------+
GUARDRAILS & SAFETY
Enterprise AI requires enterprise-grade safety controls. We implement content filtering, PII detection, prompt injection defense, and output validation — ensuring your AI assistant never goes off-script.
Trust is built by what your AI does not say, as much as what it does.
- Input sanitization, detecting and blocking prompt injection attempts.
- PII redaction, automatically masking sensitive data in both inputs and outputs.
- Topic boundaries, constraining the model to respond only within approved subject areas.
Ready to get started?
Let us know about your project and we will put together the right team and approach.