AI Development Services
Sophylabs is a Maryland-based AI development company in Rockville, MD that builds production AI agents, RAG and enterprise search, document AI, and voice agents for startups, SaaS companies, and enterprises.
Most AI projects stall between demo and production. We build the part that gets skipped — evaluations, observability, guardrails, and cost control — so your AI keeps working after launch.
Quick Answer
Sophylabs builds production AI systems — agents that act through your tools, RAG search with citations, document extraction, voice agents, and AI features inside existing software — on Anthropic Claude, OpenAI GPT-6, Google Gemini, or open-weight models. Most projects cost $20K–$50K fixed and ship in 4–8 weeks, with evals and observability included. Senior engineers, based in Rockville, Maryland.
Transparent fixed pricing
AI Integration: $20,000 – $50,000
LLM integration, RAG pipeline, chatbot, or AI workflow automation.
AI Systems We Build
Production AI in 2026 means agents that take real actions, retrieval you can audit, and quality you can measure.
Production AI Agents
Agents that plan multi-step work and act through your tools and APIs — with scoped permissions, audit logs, and human approval for high-impact steps.
MCP Servers & Tool Integration
Model Context Protocol servers that expose your systems to Claude, ChatGPT, and any MCP-compatible agent — built once, usable everywhere.
RAG & Enterprise Search
Hybrid search (keyword + vector), reranking, and access control, so answers come only from documents the user may see — with citations.
Document AI
Invoices, contracts, forms, and scans turned into structured data that matches your schema, with confidence scores and source references for review.
Voice Agents
Real-time speech agents that answer calls, book appointments, and qualify leads — handing off to a person when they should.
AI Features in Your Product
Search, summaries, drafting, and copilots added to your existing SaaS or internal tools, integrated with your auth, data model, and UI.
Evals, Observability & Guardrails
Evaluation suites built from your data, tracing of every model call, and input/output guardrails — so quality is measured, not assumed.
Migrations & Model Upgrades
Move off retired APIs (like OpenAI's Assistants API), swap or add model providers, and cut token costs — proven with before-and-after evals.
How We Build Production AI
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, and McKinsey's 2026 State of AI survey found the share of companies seeing EBIT impact from AI flat year over year at 37%. The gap is rarely the model. It is the engineering around it:
Evals before launch
We build a test set from your real data first, then measure every prompt, model, and retrieval change against it. "It looked good in the demo" is not a release criterion.
Model-agnostic by design
Each task gets the cheapest model that passes its evals — often a small fast model for routing and extraction and a frontier model only where reasoning pays off. Switching providers is a config change.
Agents with guardrails
Agents get the narrowest permissions that work, every action is logged, and anything irreversible — payments, deletions, customer emails — waits for human approval.
Costs engineered, not discovered
Prompt caching, batch processing for offline jobs, and right-sized models routinely cut token spend, with per-feature cost dashboards from day one.
Observable in production
Every model call is traced — inputs, outputs, latency, cost, and tool calls — so regressions and drift show up in a dashboard, not a customer complaint.
Your data stays yours
Commercial APIs that don't train on your data, zero data retention or HIPAA BAAs where providers offer them, or open-weight models in your own cloud.
Choosing the Right Model in 2026
No single model is best at everything, and the leaderboard changes every quarter. We benchmark candidates on your data and route each task to the model that passes its evals at the lowest cost. Current lineup as of October 2026:
| Provider | Models we use | Where it fits |
|---|---|---|
| Anthropic | Claude Opus 5.5, Sonnet 5.5, Haiku 5.5, Fable 5.1 | Agents, coding, long documents (1M-token context); Fable for the hardest long-horizon work, Haiku for high-volume tasks |
| OpenAI | GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna | General reasoning, computer use and browsing agents, cost-tiered workloads |
| Gemini 3 (Pro and Flash tiers) | Multimodal input, high-throughput workloads, Google Cloud environments | |
| Open-weight | gpt-oss, Llama 4, Mistral Small 4 and Large 3 | Data that must stay in your cloud, on-prem deployment, fine-tuning |
Every model is reachable through standard tooling — MCP for tools and data, provider SDKs, and cloud platforms such as AWS Bedrock, Google Vertex AI, and Microsoft Foundry — so you are never locked to one vendor.
AI Use Cases by Industry
Customer Support
Agents that resolve tier-1 tickets end to end — looking up orders, issuing refunds within policy, and escalating with full context.
Healthcare
Intake and prior-authorization paperwork extraction, and clinical-documentation assistants built on HIPAA-eligible model access.
Legal & Compliance
Contract review against your playbook, clause extraction, and research assistants that cite the exact source passage.
Finance & Insurance
Claims and invoice processing, reconciliation agents, and analyst copilots over filings and internal reports.
Construction & Field Services
Bid, permit, and invoice extraction plus job-history search for vertical SaaS platforms and contractors.
E-commerce
Catalog enrichment, conversational shopping assistants, and support agents connected to order and inventory systems.
Still on the OpenAI Assistants API?
OpenAI shut down the Assistants API on August 26, 2026, and retires Agent Builder and its hosted Evals platform on November 30, 2026. We migrate applications to the Responses API and Agents SDK — or to a model-agnostic stack — with an eval suite run before and after, so you can prove nothing regressed.
AI Governance, Built In
We design for the rules your buyers ask about: NIST AI Risk Management Framework practices, EU AI Act transparency obligations (in effect since August 2, 2026), Colorado's automated-decision law (effective January 1, 2027), and HIPAA-eligible model access for health data. We build the controls — logging, disclosure, human review; your counsel signs off on compliance.
Our Technology Stack
Frontier Models
Open-Weight Models
Agents & Protocols
Retrieval
Document & Voice
Evals & Observability
Stack reflects vendor documentation as of October 2026. We review it quarterly.
Frequently Asked Questions
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