AI Development Services

Build reliable AI features that ship to production — and deliver measurable ROI

SVLcode designs and engineers AI-powered products and workflow automation for teams that need business outcomes, not experiments. We deliver end-to-end execution: discovery, data strategy, RAG/agents, integrations, evaluation, deployment, monitoring, and continuous optimization.

What you can expect

• Production-grade AI architecture (security, auditability, reliability)

• Fast delivery with clear milestones and weekly demos

• Measurable outcomes: cycle-time reduction, cost-to-serve reduction, higher throughput

• Strong evaluation and observability so quality improves over time

Talk to an expert | Get an AI roadmap

What we build

AI capabilities that integrate into real operations

AI assistants and copilots

• Internal knowledge assistants (policies, SOPs, tickets, project docs)

• Customer support copilots (summaries, drafts, next-best actions)

• Sales/CS enablement (account briefs, meeting summaries, Q&A)

RAG-powered enterprise search

• Permission-aware search across internal documents and systems

• Source-grounded answers with evidence trails

• Workflow triggers (create ticket, update CRM/ERP, approvals)

AI workflow automation

• Email/document triage → extraction → validation → routing → approvals

• Exception queues with SLA ownership and escalation

• Cross-system reconciliation/matching (finance ops, logistics ops, back office)

Document AI

• OCR + extraction for PDFs/scans and multi-page packets

• Line-items/tables where needed

• Human-in-the-loop review for low-confidence fields

AWS Bedrock delivery (for AWS-first organizations)

Why we often use Amazon Bedrock

Amazon Bedrock provides a unified API to access a broad range of foundation models, with managed capabilities for building genAI applications with security and responsible AI features.

Bedrock features we implement

1) Knowledge Bases (Managed RAG)

• Managed ingestion + retrieval + prompt augmentation to reduce custom pipeline work

• Advanced chunking strategies, optional custom chunking via serverless functions

• Support for common RAG patterns used with LangChain and LlamaIndex

2) Agents (Tool-using automation)

• Agents that orchestrate model calls, retrieve context, and invoke tools/APIs to complete tasks

• Suitable for controlled automation: create/update records, generate drafts, route cases, run checks

3) Guardrails (Safety + privacy controls)

• Configurable safeguards applied consistently across models

• Input/output policies: content filters, denied topics, sensitive information protection, and allow/deny lists

4) Evaluation (Quality measurement)

• Model and RAG evaluation to measure answer quality, robustness, and retrieval correctness

• Baselines and regression testing so quality improves over time

5) Performance and cost controls

• Prompt caching to reduce latency and repeated-token costs

• Cross-region inference profiles to increase throughput and resilience

• Provisioned throughput when you need dedicated capacity for predictable performance

• Batch processing options for large document workloads

6) Model customization

• Fine-tuning or other adaptation methods when RAG alone is not sufficient

• Controlled rollout strategies to manage risk and maintain accuracy

Modern AI tech stack (production-focused)

We select components based on your constraints (security, latency, budget, data). A typical stack includes:

Orchestration and agent frameworks

• LangChain for composing LLM pipelines and tool usage

• LangGraph for stateful agent workflows (branching, retries, long-running processes)

• LangServe for deploying LLM pipelines as APIs

• LlamaIndex for RAG ingestion, connectors, and retrieval strategies

RAG foundations (retrieval and knowledge grounding)

• PostgreSQL + pgvector when you want fewer moving parts and strong control

• Dedicated vector databases such as Pinecone, Weaviate, Qdrant, or Milvus for larger-scale retrieval needs

• Hybrid search, metadata filtering, reranking, and permission-aware retrieval

Model gateways and serving

• LiteLLM as a gateway to unify multiple model providers with routing, fallback, and spend tracking

• vLLM for high-throughput self-hosted model serving when you need VPC/on-prem deployment

Guardrails, validation, and observability

• Guardrails AI (or equivalent) for structured output validation and policy enforcement

• OpenTelemetry-based tracing patterns for vendor-neutral observability

• Tools such as LangSmith (for tracing/evaluation in LangChain systems) or Langfuse (open-source observability) depending on hosting preferences

Optimization when you need systematic quality gains

• DSPy to improve prompt/program behavior using data-driven optimization rather than manual prompt tweaking

Model options (examples we deploy)

Model availability depends on region and platform configuration; we select based on the task and your constraints.

LLMs (text + multimodal)

• Amazon Nova (e.g., Nova Pro / Premier / Lite / Micro; multimodal options where needed)

• Anthropic Claude (e.g., Sonnet and Opus tiers for reasoning and complex workflows; Haiku tier for speed/cost)

• Meta Llama (e.g., Llama 3.x / Llama 4 variants depending on availability and needs)

• Mistral (e.g., Large and Mixtral families; multimodal variants where appropriate)

• Cohere Command (often strong for enterprise RAG patterns and controlled outputs)

Embeddings and reranking (for high-quality RAG)

• Amazon Titan Embeddings (text and multimodal options)

• Reranking models (to improve retrieval precision before generation)

Delivery process

Clear stages. Predictable outcomes.

1. AI discovery & roadmap
   Use case selection, data readiness assessment, success metrics, and risk controls.

2. Prototype + evaluation baseline
   A working slice of the workflow with measurable targets for quality, latency, and cost.

3. Production build
   Integrations, RBAC, audit logs, exception queues, model routing, caching, monitoring.

4. Hardening & launch
   Security review, load testing, incident playbooks, and rollout plan.

5. Optimization
   Improve retrieval quality, reduce hallucinations, lower costs, and expand coverage.

Security and compliance (enterprise-ready)

• Encryption in transit/at rest, secrets management, least-privilege access

• Permission-aware retrieval for RAG and strict tenant isolation where required

• Traceability: run traces, evaluation logs, evidence retention, and approvals

• Standardized safety/privacy controls via guardrails and governance policies

Ready to ship AI that creates real operational impact?

Tell us your workflow, systems of record, and target outcomes (cycle time, throughput, cost, quality). We’ll propose an architecture and rollout plan aligned to enterprise requirements.

Book a call | Request a proposal