Enterprise AI Engineering: Private LLM Systems & Automation

After a short paid discovery (typically 1–3 weeks) you receive a fixed price with milestones. Fixed-price pilot; platform build quoted only on measured results. Discovery is credited toward the build if you proceed.

Retrieval-Augmented Generation (RAG) dynamically retrieves relevant, factual information from your company's external databases or document repositories and injects it into the prompt context at inference time. Fine-tuning, by contrast, permanently alters the internal model weights by training the neural network on thousands of paired domain examples. In 90% of enterprise business applications, RAG is vastly superior because it provides real-time data freshness, deterministic source citations, and zero risk of model hallucination drift when documents update. Fine-tuning is reserved for teaching models specialized stylistic formats, domain vocabularies, or deterministic mathematical syntax that prompt engineering alone cannot reliably enforce. In sophisticated enterprise deployments, we frequently combine both: using a lightly fine-tuned or quantized model operating on top of a resilient hybrid RAG retrieval pipeline.

We implement an uncompromising multi-layered data protection architecture. First, we strictly utilize enterprise cloud agreements (such as AWS Bedrock, Azure OpenAI Service, or Google Cloud Vertex AI) that legally guarantee Zero Data Retention (ZDR)—meaning customer prompts and completions are never logged to disk, never reviewed by human vendor employees, and never utilized for foundation model training. Second, all incoming text passes through Microsoft Presidio and local Named Entity Recognition (NER) models running inside your VPC to detect and cryptographically redact Social Security numbers, credit card tokens, patient identifiers, and proprietary executive salaries before the payload is transmitted. For highly regulated defense, healthcare, or financial workloads, we deploy entirely air-gapped, open-weight models (such as Llama 3 or Mistral) running on private GPU infrastructure inside your corporate cloud perimeter.

Eliminating hallucinations requires objective, algorithmic evaluation frameworks rather than subjective human spot-checks. We integrate the RAGAS (Retrieval Augmented Generation Assessment) evaluation framework directly into your automated continuous integration and testing pipeline. Every system modification is programmatically scored across four core metrics: Faithfulness (measuring whether every factual claim in the answer is directly supported by retrieved context), Answer Relevance (verifying the response answers the user's specific question), Context Precision, and Context Recall against a ground-truth golden dataset. Furthermore, we implement post-generation guardrail models that evaluate the semantic grounding of the generated text, automatically catching and suppressing un-grounded assertions before they reach the end user.

The optimal vector database depends on your existing infrastructure, data scale, and query velocity. For organizations already running PostgreSQL, we strongly recommend pgvector. It allows you to store dense vector embeddings alongside your relational transactional data, eliminating the operational complexity of maintaining a separate distributed database while supporting unified SQL queries, ACID transactions, and existing backup routines. For dedicated high-concurrency search across tens of millions of vectors, we implement Pinecone Serverless or Qdrant. Pinecone provides effortless multi-region scaling with zero cluster management overhead, while Qdrant offers blazing-fast local or self-hosted performance with advanced payload filtering and hybrid search capabilities.

We engineer a comprehensive FinOps architecture to control and optimize inference expenses. First, we implement a semantic caching layer using Redis Vector Search that caches question-and-answer pairs. When a user asks a question semantically equivalent to an earlier query, the verified answer is returned in under 20 milliseconds at exactly zero token cost, typically deflecting 25-45% of total query volume. Second, we deploy dynamic model routing: simple informational queries, summarizations, and intent classifications are routed to ultra-efficient models like Claude 3.5 Haiku or Llama 3.1 8B, reserving expensive flagship models like GPT-4o or Claude 3.5 Sonnet strictly for complex multi-step reasoning tasks. Third, we establish automated per-user rate limits and monthly budget circuit breakers that prevent runaway billing surprises.

Yes. We engineer deterministic agentic function calling bridges that connect LLMs to your PostgreSQL, MySQL, Snowflake, or REST/GraphQL enterprise APIs. To ensure absolute data safety, we never allow an AI model to execute raw, unconstrained SQL queries directly against production databases. Instead, models interact with parameterized, strictly typed API tools defined via Pydantic or Zod schemas. The model generates structured JSON tool arguments, which are validated by our intermediate orchestration layer before executing read-only queries. Any state-mutating operation (such as initiating a financial refund or dispatching an email) is routed through human-in-the-loop verification consoles requiring authenticated employee approval.

Standard text extraction libraries butcher complex PDF layouts by reading across columns and destroying tabular data structures. We implement layout-aware document ingestion utilizing Vision-Language Models (VLMs) and computer vision OCR pipelines (such as Unstructured, AWS Textract, or Azure Document Intelligence). Our pipeline decomposes documents into structural bounding boxes, distinguishing between running body text, nested tables, mathematical equations, and chart graphics. Tables are extracted into structured markdown or JSON representations, preserving row and column relational semantics so the retrieval model can accurately query complex financial matrices and engineering specifications without losing row integrity.

Prompt injection—where malicious users attempt to override system instructions or extract sensitive internal prompts—is mitigated using a defense-in-depth security perimeter. We implement input sanitization that strips known injection delimiters and jailbreak patterns before prompt assembly. We deploy NeMo Guardrails and specialized safety classifiers (such as Llama Guard) that evaluate the semantic intent of incoming user queries in parallel. In addition, system prompts are authored with strict architectural separation between instructions and untrusted user data, utilizing XML tags and cryptographic delimiters. Crucially, tools and database functions available to the model are restricted strictly to least-privilege permissions, ensuring that even if an injection were theoretically successful, the model lacks the capability to execute unauthorized actions.

Your organization maintains one hundred percent exclusive ownership of all source code, orchestration logic, custom prompt templates, evaluation benchmarks, vector schemas, and fine-tuned model weights. Everything is developed inside your corporate version control repositories and private cloud tenancies. We do not retain any proprietary licensing claims, do not inject closed-source runtime dependencies, and never charge recurring software licensing fees. Upon completion, we provide complete architectural documentation, data dictionary specifications, and comprehensive engineering walkthrough sessions so your internal engineering team can independently operate, extend, and retrain the system.

Post-deployment, we offer structured service level agreements (SLAs) covering continuous model telemetry, vector index optimization, and evaluation monitoring. We configure real-time observability dashboards using OpenTelemetry, LangSmith, or Datadog to track p50/p95 latency, token consumption, semantic cache hit rates, and user feedback thumbs-up/down ratings. When upstream AI providers release new foundation models or API versions, we run regression tests against your golden benchmark dataset to evaluate accuracy and cost benefits before migrating production traffic. We also provide ongoing sprint capacity for developing new agentic capabilities and ingesting new corporate data sources.

Discovery starts within days of agreement. Builds run in weekly sprints with a demo every week.

You do, under a signed agreement — code, docs and accounts are handed over at the end.