AI Automation
Workflow automation with LLMs, logging, cost caps, and review UIs. Integrations with tools you already use.
View AI Automation service detailsRAG · Embeddings · Evals
LLM & RAG Development
Private document Q&A with citations, evals, and permission boundaries.
- Chunk, embed, retrieve, and cite with eval sets for quality drops.
- Permission boundaries per team on private document corpora.
- Abstain or escalate when confidence is low.
- pgvector, Pinecone, or Weaviate matched to scale and ops appetite.
- VPC and on-prem options discussed during discovery.
40+ projects since 2022
IST · daily sync
NDA-ready
WHAT WE OFFER
What we deliver for llm & rag development
Core deliverables
- Chunking & embedding pipelines
- Retrieval with citations
- Confidence & abstain rules
- VPC deploy options
- Quality eval harnesses
Why teams choose this engagement
- Workflow mapping and human-in-the-loop design
- Prompt, tool, and retrieval architecture
- Cost monitoring and per-tenant budgets
- Evaluation sets before production rollout
CHALLENGES
Problems we solve in llm & rag development
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Answers without citations
Support and legal teams cannot trust RAG that hallucinates sources. We require citations, confidence rules, and abstain paths.
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Wrong documents in the index
Permission boundaries must filter retrieval per user and tenant before PDFs are embedded.
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Chunking breaks on your PDFs
Tables, scans, and mixed layouts need eval on real corpora, not demo markdown files.
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VPC and data residency questions
Enterprise buyers ask where embeddings live. We document deploy options and NDA-aligned data paths upfront.
OUR APPROACH
How we build llm & rag development
Founder-led engineers in Surat (IST) with morning and end-of-day updates so distributed product owners stay in the loop.
RAG sounds simple until your PDFs are messy and answers hallucinate in front of customers. We chunk, embed, retrieve, and cite, with eval sets so you know when quality drops.
Private document Q&A for support, legal, and ops teams is our sweet spot.
Support, legal, and ops teams drowning in internal PDFs.
RAG
Private docs with citations
We build chunking, embedding, and retrieval pipelines with eval harnesses on your documents, not generic blog posts. Answers cite sources or abstain when confidence is low.
- Permission-aware retrieval per user and tenant
- Eval sets on real PDFs before production rollout
- Abstain rules for legal and support workflows
ENTERPRISE
Deploy options finance can approve
RAG for internal knowledge needs logging, cost caps, and deploy paths your security team can review. We document VPC, key management, and data retention before indexing sensitive files.
- Token and storage budgets with admin visibility
- Audit trail of queries and retrieved chunks
- Mutual NDA before document ingestion
INDUSTRIES
Where we apply llm & rag development
Vertical experience from shipped products, not generic claims.
WHY US
Why teams choose us for llm & rag development
Six reasons founders and product leads pick us over a generalist shop - scoped to how we deliver this engagement.
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Messy PDF reality
Parsing, chunking, and metadata tuned to your files.
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Hallucination controls
Retrieval limits, prompts, and human review paths.
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Eval before launch
Golden questions tracked weekly on staging.
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Support and legal sweet spot
Cited answers for ops teams, not toy chatbots.
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Eval before rollout
Golden sets and abstain rules before real users hit the feature.
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Integrates with your stack
CRM, docs, and tickets - not a standalone chat box nobody adopts.
HONEST FIT
Is this for you?
Good fit
- You have a document corpus and permission boundaries per team.
- You need citations or abstain when unsure.
- You want on-prem or VPC deploy options discussed.
- You have document corpora with clear permission boundaries.
- You need citations or abstain rules when retrieval is weak.
- You want eval harnesses before customer-facing rollout.
Probably not
- You have three blog posts and expect enterprise search.
- You have three blog posts and expect enterprise search quality.
- You want answers with no logging or cost controls.
- You cannot define which documents each role may access.
HOW WE WORK
Delivery process for llm & rag development
How we ship retrieval systems that cite sources and know when to abstain.
We document inputs, outputs, escalation paths, and data boundaries before any model keys go live. Cost caps and human review rules agreed in writing, not as a post-launch patch.
Model routing, retrieval strategy, golden test sets, and per-tenant spend limits defined upfront. Evaluation criteria signed off before pilot traffic hits staging.
Human-in-the-loop UI, logging, and token budgets on staging - real CRM, docs, and ticket integrations. Not notebook demos that break when production traffic arrives.
Abstain rules, fallback models, rate limits, and audit trails reviewed with your team. Failure modes and escalation paths tested before full rollout.
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Corpus prep
We document inputs, outputs, escalation paths, and data boundaries before any model keys go live. Cost caps and human review rules agreed in writing, not as a post-launch patch.
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Retrieval pipeline
Model routing, retrieval strategy, golden test sets, and per-tenant spend limits defined upfront. Evaluation criteria signed off before pilot traffic hits staging.
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Eval harness
Human-in-the-loop UI, logging, and token budgets on staging - real CRM, docs, and ticket integrations. Not notebook demos that break when production traffic arrives.
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Production harden
Abstain rules, fallback models, rate limits, and audit trails reviewed with your team. Failure modes and escalation paths tested before full rollout.
TECHNOLOGIES
Stack for llm & rag development
Tools and runtimes we use on this type of engagement - chosen for production delivery, not slide-deck logos.
- OpenAI
- pgvector
- Python
- FastAPI
WORKFLOW
How we work on llm & rag development
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Review queues
Human escalation UI for high-stakes model outputs.
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Cost dashboards
Token spend and error rates visible to your team.
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Incident channel
Fast loop when models drift or integrations fail.
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Eval sets
Golden questions updated as product scope evolves.
DEPLOYMENT
Production discipline for llm & rag development
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Feature flags
Model routes and prompt versions toggled without redeploying the whole app. Roll back a bad prompt in minutes, not hours.
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Spend caps
Per-tenant and global token limits enforced before production traffic. Finance sees dashboards, not surprise invoices.
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Audit logs
Prompt and tool-call history retained per your policy and NDA. Retention windows and redaction rules documented at launch.
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Review gates
Human approval on outputs above your risk threshold. Escalation UI wired before autonomous paths go live.
OUTCOMES
Track record from llm & rag development
Metrics from shipped products and active engagements - not slide-deck claims.
- 40+
- AI features in production
- Guardrails
- Human review on day one
- IST
- Morning & EOD sync
- Audit
- Logs and cost caps wired
CASE STUDIES
Proof from llm & rag development
Real products we shipped for founders in the US, UK, and Europe.
Ops and product leaders want evidence we ship LLM features with guardrails - logging, cost caps, and human review - not notebook demos.
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LLM demo failed in production
AstroSure shows LLM features with structured data, review paths, and cost controls.
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Finance saw an API bill spike
We ship token budgets and logging before real users - patterns reused below.
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No human review path
Case studies include escalation UI and audit trails, not fully autonomous agents.
Hire us
Engagement models for llm & rag development
LLM and RAG builds with fixed eval milestones before production traffic.
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Fixed-scope project
Discovery, written requirements, and milestone billing. Best for MVPs, redesigns, and integrations with a defined end state.
- Duration: Phased milestones
- Working: Sprint plan agreed upfront
- Billing: Per milestone or phase
- Timeline: Based on signed scope
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Dedicated squad
A focused engineering squad on your product: weekly demos, shared backlog, and one accountable team when scope evolves.
- Duration: 8 hrs/day · 5 days/week
- Working: ~160 hrs/month capacity
- Billing: Monthly invoice
- Timeline: Sprint-based delivery
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Part-time retainer
Smaller monthly hour buckets for fixes, dependency updates, and enhancements, with the same engineers when possible.
- Duration: 4 hrs/day · 5 days/week
- Working: ~80 hrs/month
- Billing: Monthly retainer
- Timeline: Ongoing support window
Mutual NDA before codebase access
Morning & EOD IST sync
Written scope before sprint one
FAQ
Questions about llm & rag development
What prospects ask on a first call about this service: scope, timelines, fit, and how we work.
- Corpus & citations
- Quality evals
- Vector & ingest
- Access control
5 questions
How do you scope a RAG project beyond a demo?
Corpus boundaries, citation requirements, eval sets, and human review for high-risk answers are defined upfront.
Which vector database do you recommend?
Based on scale, ops appetite, and metadata needs. We document swap costs so you are not locked blindly.
How do you measure RAG answer quality before launch?
Golden questions, failure sampling, and latency/cost dashboards on staging with your subject-matter experts.
Can RAG respect document-level permissions?
Yes. Retrieval filters by tenant and role are designed with your auth model, not bolted on after.
What happens when source documents change?
Ingestion jobs, chunk versioning, and re-index strategy are part of delivery, not a surprise maintenance bill.
GET STARTED
Building RAG for your product? Let's test retrieval.
Share document sources, privacy rules, and acceptable latency. We prototype chunking and evals in staging before you expose answers to customers.
- Retrieval quality measured - not assumed.
- Citation and refusal rules for sensitive domains.
