AI Engineering Since 2018

AI that ships, grounded in your data and your evals.

Generative AI, AI agents, RAG, computer vision, NLP, and predictive ML for SaaS, FinTech, healthcare, and enterprise. GPT-4, Claude, Gemini, Llama. Evals from day one.

AI Products Shipped
60+
AI Engineers
35+
To First AI Prototype
6 wks
Countries Served
35+
Get a free AI discoverySee AI work

Support Copilot

Grounded
What is the refund policy on annual plans?
Annual plans can be refunded within 14 days of purchase, prorated for unused months. Refunds are processed in 5 to 7 business days to the original payment method.
Sourcespolicy.md · §4.2terms.pdf · p.7

Tokens

412

Latency

820 ms

Cost

$0.004

Sample RAG response, citations always shown
AI Services

Nine AI services we ship to production.

Custom AI, generative AI, agents, RAG, computer vision, NLP, predictive ML, MLOps, and AI consulting.

  • Production grade AI

    Custom AI Development

    End to end AI product builds. Discovery, data, model selection, fine tuning, evaluation, and production deployment with observability.

    • End to end product engineering
    • Model selection and evals
    • Production deploy with monitoring
  • GPT, Claude, Gemini, Llama

    Generative AI and LLM Integration

    LLM integration for chat, search, summarization, classification, drafting, and translation. Cost guardrails and prompt registries included.

    • GPT-4, Claude, Gemini, Llama, Mistral
    • Prompt registry and version control
    • Token budget and cost guardrails
  • Multi step, tool using

    AI Agent Development

    Single agent and multi agent systems with tool calling, memory, planning, and safety rails. LangGraph, AutoGen, CrewAI, and custom orchestrators.

    • Tool calling and function exec
    • Long term memory and planning
    • Safety rails and audit logs
  • Grounded answers

    RAG and Knowledge Retrieval

    Retrieval augmented generation over your data. Embedding pipelines, vector databases, semantic chunking, reranking, and citation tracking.

    • Embedding pipelines + chunking
    • Vector DBs with reranking
    • Citation tracking and grounding
  • See and decide

    Computer Vision

    Object detection, OCR, document understanding, defect detection, video analytics. PyTorch and TensorFlow models trained on your data.

    • Detection, segmentation, OCR
    • Document and form understanding
    • Edge and cloud deployment
  • Structure from language

    NLP and Text Intelligence

    Entity extraction, classification, sentiment, intent, summarization, and topic modeling on structured and unstructured text at scale.

    • Entity, intent, sentiment
    • Domain specific classifiers
    • Multi language support
  • Tomorrow from yesterday

    Predictive ML and Forecasting

    Forecasting, churn, fraud, recommendation, scoring. Classical ML and deep learning trained, evaluated, and deployed with full pipelines.

    • Forecast, churn, fraud, scoring
    • Feature stores and pipelines
    • Drift detection and retraining
  • Ship faster, safer

    MLOps and AI Platform

    CI for models, evaluation pipelines, prompt regression, drift detection, observability, and rollback. Built on MLflow, Weights and Biases, Langfuse.

    • Eval pipelines and gating
    • Prompt and model registry
    • Cost and drift monitoring
  • Start before you build

    AI Consulting and Audit

    Use case discovery, feasibility, build vs buy, model selection, cost modeling, and risk assessment. Two to four week engagements.

    • Use case discovery and feasibility
    • Build vs buy assessment
    • Cost model and roadmap
AI Capabilities

Eight capabilities wired into every AI build.

RAG, prompts, fine tuning, embeddings, tool use, multi agent, evals, guardrails. The depth that separates demo from production.

  • Retrieval Augmented Generation

    Vector retrieval + reranking + citations to ground LLM output in your data.

  • Prompt Engineering

    Prompt registry, version control, A/B testing, and evaluation harness for prompt regression.

  • Model Fine Tuning

    LoRA, QLoRA, and full fine tuning on Llama, Mistral, Phi. Cost optimized training and serving.

  • Embeddings and Search

    Embedding pipelines with chunking strategies, hybrid search (BM25 + vector), reranking.

  • Tool Use and Function Calling

    Structured tool definitions, schema validation, error recovery, parallel tool execution.

  • Multi Agent Orchestration

    Supervisor + worker patterns, message passing, shared memory, role specialization.

  • Evaluation and Benchmarks

    Custom eval harnesses, LLM as judge, human in the loop scoring, regression baselines.

  • Guardrails and Safety

    Output validation, PII redaction, jailbreak detection, profanity filters, rate limiting.

LLM Providers

Six frontier models we route, eval, and ship.

Cost, latency, context, and license all factor in. We benchmark on your data before locking a provider.

  • GPT-4

    OpenAI

    General reasoning + tool use

  • Claude

    Anthropic

    Long context + writing + safety

  • Gemini

    Google

    Multimodal + grounding

  • Llama

    Meta

    Open weight + self hosting

  • Mistral

    Mistral AI

    European, efficient, open

  • Phi

    Microsoft

    Small + fast for edge

AI Frameworks

Eight frameworks we build production AI with.

  • LangChain

    Chain orchestration, retrieval, agents

  • LangGraph

    Stateful multi step agent graphs

  • LlamaIndex

    RAG and document indexing

  • AutoGen

    Multi agent conversation framework

  • CrewAI

    Role based multi agent teams

  • Haystack

    NLP pipelines, RAG, search

  • PyTorch

    Training and fine tuning

  • Hugging Face

    Models, datasets, transformers

Use Cases

Eight AI use cases that move the metric.

Where AI pays for itself within 6 months. Live customer cases on this list.

  1. AI Customer Support Copilot

    Agent that answers customer tickets using your help center, product docs, and CRM. Cited, accurate, and escalates when it should.

  2. Document Question Answering

    Ask questions across thousands of contracts, policies, or reports. RAG with citation, page references, and source preview.

  3. Sales Copilot and Lead Scoring

    Agent that researches leads, drafts outreach, summarizes calls, scores fit. Plugs into your CRM and email.

  4. Voice AI Agents

    Inbound and outbound voice agents with low latency speech recognition, LLM reasoning, and natural sounding TTS.

  5. Document Understanding and OCR

    Extract structured data from invoices, contracts, claims, and forms. High accuracy with human review queue for low confidence cases.

  6. Content Generation and Drafting

    Drafts for marketing, support, internal docs. Style guide enforcement, fact checking, and editorial review workflow.

  7. AI Search and Semantic Discovery

    Replace keyword search with natural language + hybrid retrieval. Faceted filters, reranking, and per user personalization.

  8. Predictive ML in Production

    Churn prediction, demand forecasting, fraud scoring, recommendation. With drift detection and retraining on schedule.

AI Tech Stack

The stack we engineer AI with.

Six layers from LLM provider to deployed model with full observability.

  1. Layer 01

    LLM Providers

    Frontier model APIs and open weight models.

    • OpenAI
    • Anthropic
    • Google
    • Meta
    • Mistral
    • AWS Bedrock

  2. Layer 02

    Vector Databases

    Embedding storage and similarity search.

    • Pinecone
    • Weaviate
    • Qdrant
    • Chroma
    • pgvector
    • Elasticsearch

  3. Layer 03

    Frameworks and Orchestration

    Agent graphs, RAG, prompt chains.

    • LangChain
    • LangGraph
    • LlamaIndex
    • AutoGen
    • CrewAI

  4. Layer 04

    ML Training and Serving

    Fine tuning, training, model serving.

    • PyTorch
    • TensorFlow
    • Hugging Face
    • vLLM
    • Triton

  5. Layer 05

    MLOps and Observability

    Evals, drift, cost, regression.

    • MLflow
    • Weights and Biases
    • Langfuse
    • LangSmith
    • Arize

  6. Layer 06

    Cloud and Infrastructure

    GPUs, autoscaling, vector store ops.

    • AWS
    • Azure
    • GCP
    • Bedrock
    • SageMaker
    • Vertex AI
Industries

AI for ten verticals.

Documented domain understanding. The hardest part of an AI build is knowing what to evaluate.

    • SOC 2
    • GDPR

    SaaS and B2B

    AI copilots, in-app agents, document Q&A.

    • PCI
    • SOC 2

    FinTech and Banking

    KYC AI, fraud, AML, advisor copilots.

    • HIPAA
    • HL7

    Healthcare

    Clinical documentation, prior auth, triage AI.

    • SOC 2
    • GDPR

    Legal and Compliance

    Contract analysis, due diligence, e-discovery.

    • PCI
    • GDPR

    E-commerce and Retail

    Recommendation, search, support copilots.

    • FERPA
    • COPPA

    EdTech

    Tutor agents, grading, content generation.

    • ISO 27001

    Logistics

    Routing, demand AI, dispatch copilots.

    • ISO 27001

    Manufacturing

    Defect detection, predictive maintenance.

    • SOC 2

    Insurance

    Claims AI, underwriting copilots, document AI.

    • GDPR

    Media and Marketing

    Content gen, audience AI, ad ops copilots.

Engagement Models

Four ways to engage our AI team.

Discovery before any build. MVP fast. Pod for scale. Project for complex. Switch any time.

Plan before you build

AI Discovery

Fixed price

From $5,000

Use case discovery, feasibility, model selection, cost model, risk assessment. Two to four week engagement.

Best for

Before any AI build starts

  • Use case prioritization
  • Build vs buy assessment
  • Cost model and timeline
  • Risk and compliance review
Start here
Most Popular

Validate fast

AI MVP Sprint

Starting at

From $20,000

Discovery to deployed MVP in 6 to 10 weeks. Real users, real data, real evals. Best for first AI bet.

Best for

First production AI feature

  • Architecture + evals upfront
  • Production grade not demo
  • Cost guardrails wired in
  • Observability from day one
Start here

Continuous delivery

AI Pod

From

$15,000 / mo

A senior AI pod (engineer + ML eng + DevOps) full time on your roadmap. Best after MVP, scaling features.

Best for

Post MVP AI roadmap

  • Senior AI engineer + ML eng
  • MLOps and observability
  • Direct Slack + standups
  • Architect oversight
Start here

Full product

Production AI Build

Fixed price

From $80,000

End to end AI product from discovery to production for complex use cases. Multi quarter engagement.

Best for

Complex AI products

  • Architecture + data + model
  • Full eval and gating pipeline
  • Production deploy with monitoring
  • 30 day post launch support
Start here
AI Process

Seven phases from discovery to production AI.

Evals on every phase, not just the last. AI is data plus prompts plus models, every part gets validated.

  1. 1 week

    Use Case Discovery

    Problem definition, success metrics, audience map, cost model, feasibility assessment.

    Deliverable

    Use case brief + KPI doc

  2. 1 to 2 weeks

    Data Audit and Prep

    Data inventory, quality assessment, labeling needs, chunking strategy, PII handling.

    Deliverable

    Data readiness report

  3. 1 week

    Architecture and Model Selection

    Provider selection, RAG vs fine tune, eval design, cost model, safety architecture.

    Deliverable

    Architecture doc + eval design

  4. 4 to 12 weeks

    Build and Iterate

    Two week sprints. Prompt iteration, RAG tuning, fine tuning, agent loops, with evals at every step.

    Deliverable

    Working AI build + eval report

  1. 1 to 2 weeks

    Evaluation and Safety

    Eval harness runs, red teaming, jailbreak testing, bias review, PII validation, cost regression.

    Deliverable

    Eval and safety report

  2. 1 week

    Production Deploy

    Deployment with observability, cost guardrails, rate limits, fallbacks, and rollback plan.

    Deliverable

    Live AI with on-call

  3. Ongoing

    Ongoing Optimization

    Live

    Drift detection, eval baselines, prompt updates, model upgrades, cost tuning, new features.

    Deliverable

    SLA support + quarterly reviews

Why Decipher Zone

Numbers AI buyers use to pick their AI partner.

01 / 04

60+

AI products in production

Copilots, RAG systems, agents, vision, voice, predictive.

02 / 04

6 wks

To first AI prototype

Tested, evaluated, real data, ready for users.

03 / 04

35+

AI engineers and ML engineers

LLM, vision, NLP, MLOps, evals, agent specialists.

04 / 04

35+

Countries served

Live AI deployments across US, UAE, EU, APAC.

ISO 9001 Certified4.9 / 5 on Clutch from 2,495 reviews100% IP ownershipOperating since 2015
FAQ

AI buyer questions, answered.

Direct answers on services, cost, time, LLMs, RAG, agents, fine tuning, safety, cost control, evaluation, and ownership.

What is an AI development services company?

An AI development services company designs and ships AI products end to end. That includes generative AI features, AI agents, RAG systems, computer vision, NLP, predictive ML, and MLOps infrastructure. The engagement covers discovery, data preparation, model selection or fine tuning, evaluation harnesses, production deployment, and ongoing monitoring with cost guardrails.

How much does AI development cost?

A discovery engagement starts at 5,000 USD. An AI MVP sprint runs 20,000 to 60,000 USD. A dedicated AI pod costs 15,000 USD per month. A full production AI build typically lands 80,000 to 350,000 USD depending on data complexity, fine tuning needs, and agent depth. We share a detailed estimate after discovery.

How long does AI development take?

A discovery engagement runs 2 to 4 weeks. An AI MVP with real users typically deploys in 6 to 10 weeks. A full production AI product runs 3 to 6 months. Multi agent systems and fine tuned models add another 2 to 4 months.

Which LLM providers do you work with?

OpenAI (GPT-4, GPT-4 Turbo), Anthropic (Claude), Google (Gemini), Meta (Llama 3), Mistral, Microsoft (Phi). We host open weight models on AWS Bedrock, SageMaker, vLLM, or Triton. We help you select based on accuracy, latency, cost, context length, and licensing needs.

Do you build RAG systems?

Yes. We design embedding pipelines with chunking strategy tuned to your content, store vectors in Pinecone, Weaviate, Qdrant, Chroma, or pgvector, run hybrid retrieval with reranking, and ship citation tracking so every answer points to the source. Evals run continuously to catch retrieval regressions.

Do you build AI agents?

Yes. Single agent and multi agent systems with tool calling, long term memory, planning, and safety rails. We use LangGraph, AutoGen, CrewAI, or custom orchestrators depending on the workflow. Every agent has eval coverage and audit logs.

Do you fine tune models?

Yes. LoRA, QLoRA, and full fine tuning on Llama, Mistral, and Phi. We optimize for cost and latency at serving time. Fine tuning is recommended only after RAG has been tested first, since fine tuning is more expensive to maintain.

How do you handle hallucinations and safety?

Multi layer defense. Grounded prompts with retrieval. Output validation against schemas. PII redaction in inputs and outputs. Jailbreak detection. LLM as judge eval pipelines. Human in the loop review for low confidence cases. Citation tracking so users can verify claims.

How do you control AI costs in production?

Token budgets per request and per user. Model routing (cheap model first, escalate to GPT-4 only when needed). Prompt caching for repeated queries. Embedding caching. Rate limiting. Daily cost dashboards with alerting when budgets exceed thresholds.

Do you do AI evaluation and benchmarking?

Yes. Custom eval harnesses, golden dataset curation, LLM as judge scoring, human in the loop validation, and regression baselines. Every prompt and model change runs against the eval suite before promoting to production. Drift detection runs daily in production.

Do you own the AI models or do we?

You own 100 percent of all training code, fine tuned model weights, prompt registries, eval suites, datasets, and infrastructure. Open weight models are licensed under their respective licenses (Llama, Mistral, Phi). API based models (OpenAI, Anthropic, Google) are governed by their API terms.

Do you serve clients in the US, UAE, Saudi Arabia, and Europe?

Yes. We work with clients across the US, UAE, Saudi Arabia, UK, Europe, and APAC. Delivery from India with business hours overlap aligned to your team. AI deployments we have shipped run in 35+ countries.
Talk to AI

Bring us the AI idea your team is afraid to scope.

A 30 minute call with a senior AI engineer, a free feasibility review, and a written architecture brief within 3 business days.

  • Free feasibility review
  • Architecture brief in 3 days
  • NDA on request
  • No obligation
Book a free AI discoverySee AI work
Free 30-minute consultation

Talk to senior engineers, not salespeople.

Share your scope. A senior developer reviews it, walks you through the trade-offs, and sends a written summary after the call. NDA before any details are discussed.

  • Written estimate within 5 business days
  • Senior engineer on the first call
  • Code stays in your repository
  • ISO 9001 certified shop
4.9 / 5from 2,495 reviews
350+ builds shipped

Talk to Senior Engineers

Available

30 minute call. Written summary after. No pitch deck.

NDA signed before any project details are shared