AI Development Services

Production-Ready AI Development Services: LLM, RAG & AI Agents

AI agents and GenAI apps built to ship, not just demo. We build LLM integrations, RAG knowledge systems and agents that take real actions in your product — with data pipelines, guardrails and monitoring from day one.

Custom LLM integrationsRAG, agents & copilotsProduction ML pipelinesSecure & compliant AI
Get a free estimate →View Our Work

Get a free estimate in 48 hours

We reply within one business day. NDA available before we talk details.

claims-agent · run 4821live
Goal: Review insurance claim CLM-20931, check the policy and route it for payout.
  1. 1Extract claim documentsocrdoc-ai 18 fields · confidence 0.97820 ms
  2. 2Retrieve policy clausesrag.search 4 cited sources310 ms
  3. 3Score risk and check for fraudflag amount 14% above typical1.2 s
  4. !Send to adjuster for approvalhuman approval policy: payout > $5,000approved
  5. 5Update CRM and notify customercrm.update audit trail written440 ms
status completedtools 5cost $0.021guardrails 3 passed
We build to these standardsHIPAAHITECH ActFDA (SaMD)EU MDRSAMHSAMIPSGDPR
4+Years of expertise
50+Apps in production
6 wksAverage MVP timeline
0NDA breaches
What we build

AI services built for real outcomes

We focus on two areas: agents that complete tasks inside your systems, and GenAI apps that answer from your own data. One team handles strategy, build and support.

AI Agents & Automation

Agents that use tools, call your APIs, update records and run workflows. Risky steps wait for a person to approve them.

  • Tool-using agentsAgents built for defined jobs such as triage, claims, reconciliation and scheduling.
  • Workflow orchestrationMulti-step and multi-agent flows that run across your CRM, ERP, EHR and ticketing systems.
  • Human approval gatesYou decide which actions need sign-off, such as payouts, refunds or messages to customers.
  • Audit trails for every actionEvery step, tool call and decision is logged and easy to review.
Automate With Agents →

GenAI Apps, RAG & Copilots

Assistants, copilots and smart search that answer from your documents and data, cite their sources and respect user permissions.

  • RAG & knowledge systemsDocument ingestion, hybrid search and reranking, cited answers and role-based access.
  • AI chatbots & copilotsSupport copilots, guided in-app workflows and internal assistants, with handoff to a person.
  • Custom LLM integrationRouting across several models with fallbacks, streaming UX, tool calling and prompt versioning.
  • AI inside your productCopilots, search and extraction added to your existing app using feature flags and rollbacks.
Build Your RAG System →

Predictive Analytics & ML

Forecasting, fraud and anomaly detection, model monitoring.

Computer Vision

Defect inspection, visual search, OCR, on-device or GPU serving.

NLP, Speech & Document AI

Extraction, call summaries, speech-to-text, intent classification.

MLOps & Model Deployment

Training and serving pipelines, drift monitoring, canary deploys.

AI Product Strategy

ROI scoring, data and privacy assessment, 90-day delivery plan.

AI MVPs for Startups

Fast prototype to production, eval harnesses, investor-ready demos.
Industries

AI that fits how your industry works

Every agent we build has a trigger, a set of allowed actions, a point where a person checks the work, and a clear result.

Copilots inside your product

Add AI to the product your customers already use: an assistant that knows your docs, smart search, and agents that automate repeat work.

  • In-product copilots
  • RAG over product docs
  • Smart search
  • Workflow agents
  • Model evals
TriggerA user asks a question or starts a task in your app
ActionsSearches product knowledge, calls your APIs, drafts or completes the task
CheckpointDestructive actions such as deletes or bulk edits ask the user to confirm
OutcomeFaster onboarding, fewer support tickets, usage tracked in analytics
Free tools

Plan your AI project in 2 minutes

Estimate what an AI agent could save you, or check whether your team is ready to build. Send us the results and we'll review them on your discovery call.

3,000
Tickets, documents or requests your team handles each month
8 min
$30/hr
Salary plus overheads, in USD
60%
The rest goes to your team, with context already gathered
Estimated yearly savings$86k
240Team hours freed per month
1.5Full-time roles freed for other work
$7,200Saved per month
4–12 monthsPayback on a $25k–$80k MVP
Today$12k/mo
With agent$4,800/mo

Indicative estimate of labour cost only. Model, hosting and support costs depend on your volume and are covered on the discovery call.

Why Tech Essentia

Built for production from day one

We plan for latency, cost, observability and failures from the start, so your AI keeps working after launch.

#1

Production-first architecture

Latency and cost budgets, failover between models, and tracing are designed in before any code is written.

fallback: gpt → claude → llama
#2

LLM, RAG & agent expertise

We work with OpenAI, Anthropic, Gemini and open-source models, and pick the stack based on your data and constraints.

route: cost · latency · residency
#3

Data pipelines you can trust

We treat ingestion, chunking, embeddings and access controls as core product work, not a side task.

acl: inherit_from_source
#4

Evals, guardrails & observability

Prompt regression tests, hallucination checks, PII filters and tracing show you how the model behaves and what it costs.

eval: groundedness · task_success
#5

End-to-end delivery

One team covers strategy, design, build, launch and support, with no handoffs between vendors.

team: strategy → support
#6

Secure, compliant deployments

Private VPC endpoints, regional data residency, SOC-minded logging and HIPAA-aware handling of PHI.

deploy: your_vpc · on_prem
How we work

Simple process, zero surprises

You get confirmed delivery dates before production coding starts.

  1. Discovery Call

    We learn your goals, data and systems, and find where AI will pay off.

    30 min · free

  2. Scope & Estimate

    A time-boxed discovery with architecture, model choice, success metrics and a fixed-scope estimate.

    1–2 weeks

  3. Agile Sprints

    Two-week sprints with working demos, evals on held-out data and your feedback each cycle.

    ~6 weeks to MVP

  4. Launch & Scale

    Production rollout with monitoring for quality and cost drift, then new use cases over time.

    ongoing
Engagements

Clear scope, clear pricing

Start with a roadmap or go straight to an MVP. You own the prompts, eval sets, fine-tunes, pipelines and code once the final payment is made.

Plan

AI Roadmap

1–2 weeksFixed fee
  • Opportunity and ROI scoring
  • Data and privacy assessment
  • Architecture and vendor choice
  • 90-day delivery plan
Plan Your AI Roadmap
Most popular

AI MVP

~6 weeks average$25k–$80k USD
  • Scoping and architecture
  • A working LLM, RAG or agent feature on your real data
  • Basic evals and guardrails
  • Launch plan
Start Your MVP
Scale

AI Platform

3–6 monthsCustom quote
  • Multi-agent workflows
  • Full integrations and SSO
  • VPC or on-prem deployment
  • MLOps and monitoring
Get an Estimate
Run

Post-launch Support

MonthlyRetainer
  • Quality and cost-drift monitoring
  • Model and prompt updates
  • Escalations within 2 hours
  • New features each quarter
Talk to Us
Our work

Not just a vendor, a growth partner

I came to Tech Essentia through a mutual contact, and honestly, I'm glad I did. What stood out from the very beginning was the attention to detail — both in design and development. Communication was easy throughout the build, which is rare with offshore teams, and the delivery was excellent. Working on Arrellio with them was one of the smoothest product experiences I've had.
Jared NobleFounder & CEO, Arrellio
Arrellio
1,000+Optical practices in the United States
Read the case study →
Trusted byArrellioAgave HealthMentalportMedTalkKidzappWest Point OpticalsGGPokerPlayrix
Technology

Any model, any cloud

We use hosted APIs or self-hosted open-source models, and route between them based on cost, latency or data residency.

LLMs
OpenAIAnthropic ClaudeGoogle GeminiOpen-source models
Agents & RAG
LangChainTool callingHybrid search & rerankingVector databases
Cloud AI
AWS BedrockAWS SageMakerAzureGoogle CloudPrivate VPC / on-prem
App stack
Next.jsReactNode.jsPostgreSQLFlutterReact Native
Enterprise systems
SalesforceServiceNowOracleMuleSoftSharePointEHRs
Security & compliance

Security-first AI, backed by global standards

We build to ISO and CMMI Level 3 practices, and ship regulated products for healthcare, fintech and enterprise teams.

GDPRHIPAAPCI-DSSNISTFISMAFDA (SaMD)EU MDRHITECH
ClutchView our Clutch profileGoodFirmsView our GoodFirms profile
  • Your data is not used to train public modelsWe default to zero-retention APIs and private endpoints, backed by contract terms.
  • Deploy in your own cloudModels run in your AWS, Azure or GCP account, or on-premises behind private networking.
  • Access follows your permissionsRAG and agents only see what the user is allowed to see in the source system.
  • PII filters and audit loggingSensitive data is filtered before prompts, and every model call and action is traced.
  • NDA before we talkWe sign an NDA before discussions, and have had zero NDA breaches.
FAQ

Common questions, straight answers

Can't find what you need? Ask us on the discovery call.

Ask Us Anything →
Do you build AI agents that can take actions, not just chat?

Yes. Our agents call APIs, update records and trigger workflows. High-risk steps need human approval, and every action is logged.

How long does it take to build an AI product?

Our average MVP ships in about 6 weeks when the use case and data are clear. Larger platforms take 3–6 months. Discovery is time-boxed, and you get confirmed delivery dates before production coding starts.

What's the typical cost for an AI MVP?

Usually $25,000–$80,000 USD. That covers scoping, a production-ready integration or RAG feature, basic evals and a launch plan. Computer vision, complex agents or GPU clusters raise the cost.

Will our data be used to train public models?

No, unless you explicitly opt in. By default we use zero-retention APIs and private endpoints, with contracts that keep your data out of public training.

Can you build RAG over our existing documents and systems?

Yes. We connect to wikis, tickets, CRMs, EHRs, SharePoint and databases. Access control follows the permissions in each source system.

Can you add AI to our existing product?

Yes. Most of our projects add copilots, search, extraction and automation to existing apps, using APIs, feature flags and rollbacks.

Who owns the models, prompts and code?

You do. After the final payment you own the prompts, eval sets, fine-tunes, pipelines and application code, and we keep no licence to them.

How do you measure whether the AI is actually good?

We agree on success metrics up front, such as groundedness, task completion, latency and cost per request. We run evals on held-out data during development and trace quality and cost in production.

How does your team handle US business hours?

Our team overlaps with US Eastern and Pacific mornings, usually 8 AM–1 PM EST. That covers standups and Slack replies, and urgent issues are escalated within two hours.

Let's work together

Got an AI idea but not sure where to start?

Book a free 30-minute discovery call. We'll tell you if AI is a good fit, which approach to take, and what an MVP would cost.

  1. We reply within 24 hours

    A senior engineer, not a salesperson, reviews your request.

  2. 30-min discovery call

    We go through your process, data and goals under NDA.

  3. Fixed-scope estimate

    You get the scope, timeline and cost in writing.

Email[email protected]
Phone (US)+1 (912) 758-3449
WhatsApp+91 7006282292

Book a Free Discovery Call

Step 1 of 3What you want to build

We reply within 24 hours. Your details are only used to respond to this request.

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