We build, ship, and scale intelligent software—safely and fast. Prodhee Technologies partners with enterprises and digital-native companies to design, develop, and operate AI systems that drive measurable impact across functions. From rapid MVPs to production-grade platforms, our nearshore + onshore model delivers outcomes, not experiments.
Trusted by
AI Services
Our work spans model selection, data preparation, evaluation, safety, deployment, and lifecycle operations. Below are the core services most teams start with. Each tile should use a clean icon + 1-line value statement, with hover states that reveal details.
Applied & GenAI
Turn strategy into shipped capability. We connect AI to concrete business KPIs—reducing lead times, errors, and costs while improving customer and employee experiences.
● What we do: Opportunity mapping, ROI modeling, use-case chartering, build vs. buy analysis, success metrics & guardrails, solution architecture, rapid prototyping.
● Where it fits: When you need impact beyond a chatbot—search, summarization, content ops, decision support, forecasting, knowledge discovery.
● Deliverables: Executive brief, solution blueprint, prioritized backlog, architecture diagrams, privacy/threat model, MVP plan.
● Typical outcomes: 20–50% cycle-time reduction, 15–30% cost per task reduction, higher CSAT/NPS coverage via AI-augmented workflows.
Agentic AI
Autonomous, auditable agents that get real work done.
● What we do: Task decomposition, multi-agent orchestration, tool-use planning, safety rails (critics/validators), memory and long-horizon planning, human-in-the-loop.
● Use cases: Customer ops triage, quote & proposal generation, order management, procurement follow-ups, finance close prep, IT runbooks, marketing ops.
● Deliverables: Agent specs, tool registry, evaluation harness, runbooks, control-plane dashboards, escalation flows.
● KPI impact: Lower average handle time (AHT), increased SLA compliance, reduced rework, consistent quality.
Computer Vision
See, detect, and decide in real time.
● What we do: Classification, detection, segmentation, OCR/ICR, video analytics, multimodal fusion, edge deployment on GPUs/NPUs.
● Use cases: Visual QC, barcode/label reading, shelf analytics, safety monitoring (PPE), traffic/parking analytics, document intake (KYC, invoices).
● Deliverables: Data labeling strategy, model cards, edge packaging, monitoring metrics (precision/recall), latency budget, MLOps pipelines.
● KPI impact: Fewer false rejects, increased throughput, reduced manual inspection costs.
Generative AI
Create at scale—safely and on brand.
● What we do: Text/image/audio generation, prompt engineering, structured output via function/tool calls, brand & compliance filters, watermarking options.
● Use cases: Product descriptions, knowledge base drafts, marketing variants, code suggestions, voice bots.
● Deliverables: Prompt libraries, style guides, safety filters, review workflows, usage dashboards.
● KPI impact: Reduced content lead time by 40–70%, increased editorial throughput, improved governance auditability.
LLM Fine‑Tuning
Specialize models for your domain.
● What we do: Data curation/augmentation, SFT, DPO/ORPO, LoRA/QLoRA, parameter-efficient strategies, evaluations, red-teaming.
● Use cases: Domain-specific chat/assistants, classification, extraction, tool-use reliability.
● Deliverables: Training datasets, experiment tracker, model weights/adapters, model card, reproducible training scripts.
● KPI impact: Increased factuality, reduced refusal/over-reach, lower latency and cost per request versus larger base models.
LLM Model Evaluation
Trust what you deploy.
● What we do: Golden sets, rubrics, automated judges, task-level evaluations, hallucination/grounding checks, adversarial probes, regression gates.
● Use cases: Pre-launch gates, continuous quality monitoring, vendor model benchmarking.
● Deliverables: Evaluation harness (CI-ready), dashboards, bias & safety report, data drift alerts, release checklist.
● KPI impact: Reduced quality variance, fewer escaped defects, shorter time-to-approve releases.
Model Context Protocol (MCP) Development
Standardized, secure tool access for AI systems.
● What we do: MCP server/tool design, capability schemas, authz/authn, rate-limited tool adapters (DBs, SaaS, internal APIs), sandboxing.
● Use cases: Agent access to enterprise systems (ERP/CRM), retrieval plugs, workflow automations with audit trails.
● Deliverables: MCP servers/tools, access policies, observability hooks, change-management documents.
● KPI impact: Faster integrations, safer tool use, consistent governance across applications.
Natural Language Processing (NLP)
Understand and act on unstructured text.
● What we do: Classification, NER/extraction, summarization, sentiment, topic
modeling, speech‑to‑text & text‑to‑speech, multilingual.
● Use cases: Email/CRM triage, policy/contract analysis, voice IVR
modernization, compliance monitoring, insights mining.
● Deliverables: Data pipelines, models/services, quality dashboards,
integration SDKs.
● KPI impact: Manual review time ↓, coverage ↑, compliance breaches ↓.
AI Services
We turn ambiguity into shipped software through a transparent, testable flow.
Discovery & AI Readiness
Stakeholder interviews, data & process mapping, risk register, ROI model, success metrics, and a go/no‑go decision.
Experimentation & Prototyping
Spike different models/approaches, build thin‑slice demos, run user studies.
Pilot / MVP
Build the smallest valuable product, instrumented for usage, quality, and cost telemetry.
Hardening & MLOps
CI/CD, eval gates, data versioning, secret management, rollback strategy, SLOs, observability (quality, cost, latency, safety).
Security, Privacy & Governance
PII handling, red‑teaming, model cards, audit logs, access policies, on‑prem/VPC isolation options.
Scale & Continuous Improvement
Cost tuning, prompt/model drift alerts, auto‑retraining triggers, feature roadmap.
Platforms, Security & Team: Comprehensive AI Delivery Stack
Platforms & Tooling
Foundation & Small Models: OpenAI, Anthropic, Google Gemini,
DeepSeek, LLaMA, Mistral.
● Frameworks & Orchestration: LangChain, LlamaIndex, OpenAI Realtime,
MCP, function/tool calling, serverless agents.
● RAG & Search: Pinecone, Weaviate, pgvector, Elastic, Vespa, OpenSearch
hybrid.
● Data & Pipelines: Airflow, Databricks/Spark, Snowflake, Kafka, dbt.
● Vision: OpenCV, Ultralytics, Torch/TensorRT, ONNX.
● MLOps & Observability: MLflow, Weights & Biases, Evidently, TruLens,
Promptfoo, LangSmith.
● Deploy: Kubernetes, serverless, edge (Jetson/Coral), multi‑cloud or on‑prem/VPC.
Security & Governance
● Data: Minimize, mask, and encrypt; regional residency; zero‑retention options.
● Access: SSO/SAML/OIDC, least‑privilege, approval workflows for tool/agent
access.
● Safety: Abuse filters, jailbreak defenses, content moderation, rate limits, task
timeouts.
● Compliance: Support for SOC 2, ISO 27001, HIPAA, GDPR; model cards &
DPIA templates.
● Observability: Quality, cost, latency, and safety dashboards; audit trails for
MCP tool use.
The Team You Get
● Beyond great developers, you get a complete delivery layer.
● Roles: AI Architect, ML Engineer, Data Engineer, Data Scientist, MLOps
Engineer, NLP Engineer, Computer Vision Engineer, Product Manager,
QA/SDET, Delivery Manager.
● Working Model: Daily stand‑ups, weekly reviews, sprint demos, transparent
burn‑up charts, proactive risk logs, built‑in redundancy.
Case Studies
Built RAG + agents on top of 1.2M documents. Outcomes: 55% ↓
time‑to‑answer, 90% answerability with citations, onboarding time ↓ 35%.
Deployed edge models with sub‑70ms latency. Outcomes: False rejects ↓
40%, throughput ↑ 25%, manual hours ↓ 60%.
Multi‑agent system to draft, review, and risk‑check proposals using MCP tools
for CRM/ERP. Outcomes: Cycle time ↓ 48%, compliance flags ↓ 30%.
FAQ
How do you choose between RAG and fine‑tuning?
Start with RAG for grounding and governing knowledge. Introduce fine‑tuning when
style, tool‑use reliability, or domain specialization matter and your data supports it.
Can you run fully inside our VPC/on‑prem?
Yes. We support no‑internet egress, customer‑managed keys, and zero‑retention
settings.
What does an evaluation plan look like?
Golden datasets, task rubrics, bias/safety probes, regression gates in CI, and
dashboards for quality/cost/latency.
How do you keep costs under control?
Latency/cost budgets, request shaping, caching, distillation/PEFT, and model routing
based on task difficulty.
What about data privacy and IP ownership?
You own your data, code, and artifacts. We provide model cards, DPIA templates,
and audit logs for compliance.
How fast can we see value?
Most teams reach a decision‑quality demo within the first 2–4 weeks of a structured
discovery + prototype.