RAG Knowledge Assistants
Chatbots and Q&A assistants that answer from your documents, wikis, tickets, and product data — with citations, not guesses. We handle ingestion, chunking, embeddings, hybrid retrieval, and grounded generation.
AI Engineering
Production-Grade AI Systems, Built on Your Data
We design and ship retrieval-augmented generation (RAG) systems, AI assistants, and agentic workflows that run reliably in production — grounded in your own content, integrated with your existing stack, and engineered to control cost, latency, and hallucination. From first proof-of-concept to scaled, monitored deployment, we own the full lifecycle.
10+ years of software engineering behind every build — versioned, tested, observable, and secure.
What We Build
AI Services That Ship to Production
End-to-end engineering across the AI lifecycle — from data pipelines to evaluation and MLOps.
AI-Powered Search & Recommendations
Semantic and hybrid search for e-commerce and content platforms: natural-language discovery, "more like this" recommendations, and AI merchandising that lifts conversion. Built to plug into Magento, Shopify, and WordPress.
Document & Data Extraction
LLM pipelines that read invoices, contracts, forms, and reports and turn them into structured, validated data — with confidence scoring and human-in-the-loop review for the cases that need it.
Custom LLM & Agentic Workflows
Multi-step AI agents that take action across your systems — calling APIs, querying databases, and orchestrating tools to complete real tasks. We design the guardrails, retries, and fallbacks that make autonomy safe.
LLM Integration & Fine-Tuning
Integrate frontier and open-weight models (OpenAI, Anthropic, Llama, Mistral) with prompt engineering, structured outputs, and fine-tuning where it earns its cost. Vendor-neutral — we recommend what fits your targets.
LLMOps & Evaluation
The unglamorous work that keeps AI trustworthy: evaluation harnesses, regression tests for prompts and retrieval, observability, cost and token monitoring, and CI/CD for model changes. We make AI measurable.
Our Process
How We Build RAG Systems
A disciplined path from raw data to a monitored production system.
1
Discovery & Data Audit
We map your knowledge sources, data quality, access patterns, and success metrics before writing a line of code.
2
Ingestion & Indexing
Document parsing, cleaning, chunking strategy, embedding model selection, and vector index design.
3
Retrieval Design
Hybrid (semantic + keyword) retrieval, re-ranking, metadata filtering, and context assembly tuned for your content.
4
Generation & Guardrails
Grounded prompting, citation enforcement, structured outputs, and hallucination controls.
5
Evaluation
Golden datasets, automated accuracy and faithfulness scoring, and human review loops before launch.
6
Deploy & Monitor
Scalable serving, caching, cost controls, observability, and continuous evaluation in production.
Case Studies
Selected AI Engagements
Client names withheld and sectors described generically to protect confidentiality.
RAG Knowledge Assistant for an Enterprise Support Team
The Challenge
A B2B software company's support agents were spending hours hunting through thousands of help articles, release notes, and internal runbooks scattered across multiple systems. Answer quality varied by agent, onboarding took months, and ticket resolution times were climbing as the product grew.
Our Solution
We built a retrieval-augmented assistant that answers agent and customer questions directly from the company's own knowledge sources, with every answer linked to its source document. A scheduled ingestion pipeline keeps answers current as the product evolves.
- Ingestion: Automated pipeline parsing HTML, PDF, and Markdown sources with structure-aware chunking.
- Retrieval: Hybrid semantic + keyword search with a cross-encoder re-ranker and metadata filtering by product area and version.
- Generation: Grounded prompting with enforced citations and a confidence threshold that routes low-confidence queries to a human.
- Evaluation: A golden-question test set with automated faithfulness and relevance scoring gating every deployment.
Stack: Python, FastAPI, OpenAI + open-weight embeddings, Qdrant vector store, hybrid retrieval with re-ranking, Langfuse for tracing and evaluation, AWS serverless deployment.
Results
| Metric | Outcome |
|---|---|
| Average ticket resolution time | Reduced by ~40% |
| First-response accuracy | Improved, with source citations on every answer |
| New-agent ramp-up | Cut from months to weeks |
| Answer grounding | Traceable to source — near-zero unsupported claims in eval |
AI Semantic Search & Recommendations for an E-Commerce Retailer
The Challenge
An online retailer with a large, fast-changing catalog was losing sales to poor on-site search. Shoppers searching in natural language ("lightweight waterproof jacket for hiking") got weak keyword-only results, and the "related products" widget showed irrelevant items. Search-exit rates were high and conversion from search was below benchmark.
Our Solution
We layered semantic search and embedding-based recommendations onto the existing storefront without replatforming. Product data was enriched and embedded so the catalog became searchable by meaning, and recommendations were driven by genuine product similarity and behavioral signals.
- Semantic search: Hybrid retrieval combining vector similarity with keyword matching and business rules (stock, margin, promotions).
- Recommendations: Embedding-based "more like this" and complementary-item suggestions, refreshed as the catalog changes.
- Merchandising controls: Admin controls to boost, bury, and pin products without code changes.
- Integration: Connected to the existing storefront via API with sub-second response times and caching.
Stack: Node.js, Python embedding pipeline, pgvector + OpenSearch hybrid index, Redis caching, storefront integration via REST API; built to slot into Magento / Shopify front-ends.
Results
| Metric | Outcome |
|---|---|
| Search-to-conversion rate | Increased by ~25% |
| Search-exit (zero-result) rate | Reduced significantly |
| Average order value | Lifted via relevant recommendations |
| Time to deploy | Live in 8 weeks, no replatforming |
Intelligent Document Extraction for a Finance Operations Team
The Challenge
A finance operations team was manually keying data from thousands of invoices and supporting documents each month. The documents arrived in inconsistent formats from many vendors, manual entry was slow and error-prone, and exceptions piled up — creating a bottleneck that scaled linearly with volume.
Our Solution
We built an LLM-based extraction pipeline that reads incoming documents, extracts the required fields into a validated structured schema, and flags only genuinely ambiguous cases for human review. Confident extractions flow straight through; only edge cases reach a person.
- Parsing: Layout-aware OCR and document parsing handling varied vendor formats.
- Extraction: LLM extraction into a strict schema with structured-output validation and per-field confidence scoring.
- Human-in-the-loop: A review queue surfacing only low-confidence fields, with corrections feeding back into evaluation.
- Validation: Business-rule checks (totals, tax, vendor matching) before data is committed downstream.
Stack: Python, document parsing + OCR, LLM extraction with structured outputs and schema validation, confidence scoring, PostgreSQL, queue-based processing, audit logging for compliance.
Results
| Metric | Outcome |
|---|---|
| Manual data-entry effort | Reduced by ~80% |
| Straight-through processing | Majority of documents fully automated |
| Extraction accuracy | High, with confidence-gated human review |
| Processing throughput | Scales with volume without added headcount |
Agentic Workflow Automation for an Operations-Heavy Business
The Challenge
A growing company ran a repetitive, multi-step back-office process spanning several internal tools — gathering information, cross-checking records, drafting responses, and updating systems. The work was rules-based but too nuanced for traditional automation, and it consumed a large share of the team's time.
Our Solution
We designed an AI agent that executes the workflow end to end, calling internal APIs and databases to gather and verify information, then drafting outputs and updating records — with guardrails, approval gates, and full traceability. The agent operates within defined boundaries, escalates anything outside its confidence range, and logs every action for audit.
- Orchestration: A planner-executor agent that decomposes tasks and calls tools (APIs, DB queries, document generation) in sequence.
- Guardrails: Scoped permissions, validation at each step, retries with fallbacks, and human approval before any irreversible action.
- Observability: Full execution traces, token/cost monitoring, and replayable runs for debugging.
- Integration: Connected to existing internal systems via secure APIs with role-based access.
Stack: Python, agent orchestration framework, tool/function calling, PostgreSQL, secure API integrations, LangSmith/Langfuse tracing, containerized deployment on Kubernetes.
Results
| Metric | Outcome |
|---|---|
| Time spent on the process | Reduced by ~60% |
| Throughput | Higher, with consistent quality |
| Error rate | Lowered via step-level validation |
| Auditability | Every action logged and replayable |
Our AI Stack
Engineered on Proven Tools
Vendor-neutral choices driven by your accuracy, latency, and budget targets.
Models
OpenAI GPTAnthropic ClaudeGoogle GeminiMeta LlamaMistral
Frameworks
LangChainLlamaIndexHaystackCustom orchestration
Vector & Search
PineconeWeaviateQdrantpgvectorElasticsearch / OpenSearch
Data & Backend
PythonNode.jsFastAPIPostgreSQLRedis
Infra & MLOps
AWS / GCP / AzureDockerKubernetesServerless inferenceLangSmith / Langfuse
Integration
REST / GraphQL APIsWebhooksMagentoShopifyWordPress
Let's Build
Have a use case in mind?
Whether you're validating a first AI proof-of-concept or scaling a system to production, we can help you scope it, build it, and ship it responsibly. Tell us about your data and your goal — we'll come back with a clear, technical recommendation.