Production-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
We help enterprises embed large language models into products and internal workflows — with retrieval-augmented generation, evaluation harnesses and cost controls built in from day one.
Most LLM projects fail not because the model is wrong, but because the surrounding system is missing. T7 Solution builds the retrieval layer, guardrails, evaluation loops and observability that turn a prompt into a product.
We work across OpenAI, Anthropic, Google, Meta Llama, Mistral and self-hosted stacks — choosing the right model per task, not per hype cycle. Our RAG pipelines are grounded in your documents, databases and APIs, with citations and traceability by default.
Every deployment ships with an evaluation suite, prompt versioning, PII redaction and a cost dashboard — so you can scale usage without scaling risk.
Vector search, hybrid retrieval, re-ranking and chunking strategies tuned to your corpus.
Domain adaptation on Llama, Mistral and GPT — plus distillation to smaller, cheaper models.
Structured prompts, function calling, JSON-mode outputs and automated evaluation with golden datasets.
PII redaction, jailbreak detection, output validation and human-in-the-loop review.
Caching, model routing, batching and quantisation to cut inference cost 40–80%.
Trace-level logging, token accounting, drift detection and A/B testing for prompts and models.
Company-wide Q&A over policies, contracts, product docs and Confluence.
Draft replies, summarise tickets, surface next-best-action from CRM data.
Replace keyword search with meaning-based search across unstructured content.
Brand-safe marketing, product descriptions and personalised outreach at scale.
SaaS / Analytics
The support team was drowning in repetitive product questions and analysts spent hours writing SQL for one-off reports — while the CFO wanted every AI initiative tied to a P&L number.
T7 shipped a RAG copilot grounded in product docs, changelog and the customer data warehouse. Model routing sends simple questions to a small local model and complex reasoning to GPT-4-class models. Every response ships with citations and a confidence score; low-confidence answers escalate to a human.
Deflection on Tier-1 support crossed 68% within 90 days. Analyst SQL time dropped by roughly half. Optimized query routing and semantic caching ensured latency remained sub-second.
"T7 didn't sell us a chatbot — they built the retrieval, evals and cost controls we didn't know we needed. It's the first AI project we've shipped that our CFO actually likes."
We audit your data, workflows and success metrics — and choose the right model + retrieval strategy.
Working RAG or fine-tuned prototype in 2–3 weeks, with an eval harness from day one.
Guardrails, observability, cost controls and CI/CD for prompts and models.
Ongoing evals, model upgrades and continuous cost/quality tuning.
It depends on task, data sensitivity and cost. We benchmark 2–3 candidates on your data before committing. Many production stacks route between models per query type.
Retrieval grounding with citations, structured outputs, validation layers, and automated evals against a golden dataset. We measure hallucination rate, not just anecdotes.
Yes. We deploy Llama 3, Mistral and other open-source models on your cloud or on-prem GPUs when data residency or cost demands it.
A working prototype typically takes 2–4 weeks. Production deployment with guardrails and observability adds 4–8 weeks depending on scope.
Model routing, semantic caching, prompt compression, batching and — where quality allows — distillation to smaller models. We publish a cost dashboard so every team owns their spend.
Ingestion → chunking → embeddings → vector store (pgvector, Pinecone or Weaviate) → hybrid retrieval + re-rank → LLM with structured output → eval + trace logging. We tune each stage against your corpus.
Yes. We do supervised fine-tuning, LoRA/QLoRA on open-source models, and preference tuning (DPO/RLHF-lite) when you have labelled feedback data.
Data stays in your cloud region, PII is redacted before it hits third-party APIs, and every request is logged with lineage. We ship the DPIA template and vendor DPAs as part of go-live.
Yes — Snowflake, BigQuery, Databricks, Redshift and Postgres are all first-class. We build ELT for the RAG index and keep it in sync via CDC or scheduled refresh.
Monthly evals, drift monitoring, prompt/version rollouts, cost tuning and quarterly model upgrades. Most clients stay on a managed-services retainer.
Custom AI chatbots trained on your data — web, WhatsApp, Slack and beyond.
Learn more about AI Chatbots & Conversational AgentsMulti-agent architectures that plan, use tools and complete complex tasks.
Learn more about Agentic AI SystemsAI-native automation that reads, decides and acts across your systems.
Learn more about Intelligent AutomationWe deliver llm integration & rag across our core markets, with on-site discovery and local timezone support.
Use cases we ship it in, industries that buy it, insights behind it, and comparisons to reason through.
Deflect 60%+ of tier-1 tickets without hurting CSAT.
Draft first-pass underwriting notes with citations, in minutes.
AI for talent, onboarding and employee self-service.
From AI prototype to production
AI copilots for law firms and in-house teams
AI engineering is the discipline of turning models, data and tools into reliable business systems. Here's what it actually covers, how it differs from traditional software engineering, and where the ROI shows up.
Model routing, prompt compression, caching, distillation and eval-driven downgrades — the levers we use to bring enterprise LLM bills under control without hurting quality.
How OpenAI's GPT models compare to Anthropic's Claude for enterprise workloads
OpenAI's GPT-5 vs Google's Gemini 2.5 Pro for enterprise
Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.