A generative AI development company for agents that act, not just answer
We build retrieval-grounded assistants, copilots and multi-agent workflows that read your documents, call your systems and complete tasks under your risk team's rules. As a generative AI development company we treat an agent as an orchestrated system: a model, permitted tools, a human approval step and an audit trail. The model is the smallest part.
Why generative AI prototypes fail the move into the enterprise
A prompt and a model endpoint make a convincing demo in an afternoon. What they do not give you is grounding in your actual documents, a permission model that keeps the assistant out of the wrong folder, a record of what it did and why, or anyone accountable when it is wrong.
Those gaps stall most generative AI programs after the first pilot. They are engineering problems, so we define the boundary, build the controls, test against failure cases and give the system an owner before it meets a user.
What is agentic AI and why does it matter to your operations?
Generative AI produces text, code or structured output from a language model. Agentic AI goes further: an orchestrator plans a task, calls tools such as a database query or an ERP posting, checks the results and asks a person before anything irreversible happens. It matters because the value in most back offices sits in multi-step work, not single answers.
We build these systems with graph-based orchestration such as LangGraph, where every step, branch and retry is explicit and inspectable. Agents reach live systems through MCP-style connectors, so a tool is a declared capability with a schema and a permission, not a hidden API key in a prompt. We have delivered LangGraph agent systems for document-heavy and hospitality workflows.
Our LLM application development and agentic AI services
Six services that usually run as one program. Consulting decides what to build; evaluation and LLMOps decide whether it stays in production.
Enterprise generative AI consulting
We rank use cases by data sensitivity, document quality, token economics and the cost of a wrong answer, then recommend the model hosting pattern your regulator will accept: your cloud tenancy, a regional endpoint or on premises.
Retrieval-augmented generation
Assistants grounded in your policies, contracts, tickets and databases, with permissions enforced at query time so users only see answers built from documents they could already open. Chunking, hybrid search and citation are tuned on your corpus.
AI agents and multi-agent workflows
Orchestrated agents on LangGraph-style state machines with scoped tool access, MCP-style connectors to ERP, CRM and ticketing, human approval for writes and payments, and recovery paths when a tool call fails.
Conversational AI and voice systems
Chat and speech interfaces in English, Arabic and Indian languages that complete bookings, orders and status checks, with speech recognition, text to speech and telephony where needed.
LLM application development
Copilots embedded in the tools your people use, extraction interfaces that turn invoices and agreements into structured records, and generation workflows with deterministic validation before anything is sent or posted.
Evaluation, safety and LLMOps
Test sets for retrieval accuracy, groundedness and task completion; prompt injection and exfiltration testing; latency and cost budgets; regression suites on every prompt, model or connector change.
Enterprise use cases we have built or scoped
Real patterns from our delivery work, without client names or figures.
Internal policy and knowledge assistants
A retrieval-grounded assistant over HR, finance and IT policies with role-based access, so a manager and a new joiner get different answers to the same question. See enterprise knowledge intelligence.
Recruitment and candidate-interaction workflows
An agent that screens applications against a role profile, drafts outreach, schedules interviews through a calendar connector and hands every shortlist decision to a recruiter, with reasoning logged.
Support desk agent assistance
A copilot inside the ticketing tool that summarizes the thread, retrieves similar resolved cases and proposes a reply the engineer edits. Nothing reaches the customer without a person clicking send.
Invoice and agreement analysis
Vision language models extracting fields from Arabic and English invoices, a judge model scoring confidence and routing doubtful documents to review, feeding our intelligent document processing pipeline.
Natural-language business intelligence
Plain-English questions translated to governed SQL over a semantic layer, so "revenue by region" uses the board report's definition. Generated queries are shown, not hidden.
Multi-step back-office task coordination
A LangGraph agent that takes a hotel guest request from chat, checks availability, places the order in the property system and confirms back, with staff approval for refunds. See agentic process automation.
How we deliver a generative AI program in seven steps
Seven steps from use-case selection to monitored operation. The evaluation baseline at step three is the gate: if grounded accuracy on your documents is not good enough, we fix the data or stop, and we tell you which.
- 01
Use-case selection
Rank candidates by value, document quality and cost of a wrong answer.
- 02
Data and permission mapping
Which sources, who may see them, how access is enforced.
- 03
Evaluation baseline
A labeled test set and first measurement of answer quality.
- 04
Agent and tool architecture
Orchestration graph, connectors, approvals, audit design.
- 05
Controlled pilot
Small user group, real documents and systems, every action reviewed.
- 06
Human-in-the-loop deployment
Wider rollout, approval thresholds tuned from pilot evidence.
- 07
Monitoring
Quality, cost, latency and override rates tracked; regression tests on change.
Where we deliver: from Chennai, Bangalore, Abu Dhabi, Saudi Arabia and Branchburg, New Jersey. In India we design to the DPDP Act 2023. In UAE, UAE PDPL governs where prompts and documents may be processed. In Saudi Arabia, SDAIA's PDPL and NCA ECC-2:2024 shape model hosting, and Vision 2030 programs drive demand. In United States we work to the NIST AI Risk Management Framework. See our locations.
Business outcomes and how we measure agent quality
A generative AI system is succeeding when answers are grounded, actions are authorized and people trust it enough to stop checking every output. We measure groundedness and citation accuracy on a labeled set, task completion rate, the share of actions a human overrides, latency and cost per task, and safety test cases passing on every release.
The business numbers come from the process the agent sits in: handling time, backlog or days to post an invoice. Both sets go on one dashboard so a drop in retrieval quality shows before the business metric moves.
Security, governance and quality for generative AI
Every assistant and agent runs with least-privilege tool access in approved environments, with prompts, retrieved context, tool calls and outputs logged to an audit store you control. Permissions are enforced at retrieval time, not by asking the model to behave.
We test for prompt injection, data exfiltration through tool calls and over-broad connector scopes before pilot, and re-run those tests on every change. Sensitive actions require a named approver. Regulated workflows add legal, security and compliance review. Our practices are ISO 27001-aligned.
Why Constient for agentic AI development
We have shipped agents into production. These six habits come from that experience.
Orchestration, not prompting
Agents are explicit graphs with state, retries and approval nodes, so behavior is inspectable and testable rather than hidden in a long prompt.
Connectors with contracts
Tools are declared through MCP-style connectors with schemas and scopes, which is how your security team can say yes to an agent touching the ERP.
Data foundation included
Retrieval is only as good as the corpus. Our data engineering team prepares, deduplicates and governs the documents before an assistant sees them.
Products as proof
Our AI Digital Employee and hospitality assistants use the orchestration patterns we propose to you, so you can see them running first.
Multilingual by design
English, Arabic and Indian-language text and speech are first-class requirements in our markets, not an afterthought.
Honest evaluation
If grounded accuracy on your documents is not good enough, the baseline report says so and proposes the data fix, not a bigger model.
Frequently asked questions
What makes an AI agent different from a chatbot, in practice?
A chatbot answers. An agent plans a task, calls permitted tools such as a database query or an ERP posting, checks the result and asks a person before anything irreversible happens. We build agents as orchestrated systems with explicit state, scoped connectors, approval steps and an audit trail. The difference is governance and action, not a smarter reply.
Can agents access our internal systems safely?
Yes, through declared connectors with schemas, scopes and credentials your IT team issues, never through credentials pasted into prompts. Each tool call is logged, writes and payments require a named approver, and we test connector scopes for over-reach before pilot. Agents reach Dynamics 365, ticketing tools and databases this way.
Will the outputs always be correct?
No, and we design for that. Retrieval grounds answers in your documents with citations, deterministic validation checks extracted values against master data, low-confidence outputs go to a human queue, and evaluation sets measure groundedness on every release. The goal is a known, monitored error rate with an escalation path.
Where will our documents and prompts be processed?
Inside the jurisdiction your regulator requires. We deploy models through your Azure, AWS or Google Cloud tenancy in a regional endpoint, or on premises where sensitivity demands it. For Saudi and UAE clients we keep processing in-country from the first design; for Indian clients we align to the DPDP Act 2023.
How long does a generative AI engagement take to show results?
Use-case selection and permission mapping take 2 to 3 weeks. An evaluation baseline on your documents follows within another 2 to 3 weeks and tells you whether to proceed. A controlled pilot with real users typically runs 6 to 8 weeks after that, depending on document quality and connector readiness.
How is generative AI development priced?
Consulting and the evaluation baseline are fixed price. Pilot and production builds are priced per phase against acceptance criteria agreed in advance. Ongoing operation runs as a dedicated team or retainer, and we estimate model usage and infrastructure cost alongside the build cost.
What do you need from us to begin?
A named business owner for the use case, a representative sample of documents or data, a security contact who can approve connector scopes, and a few hours from the people who do the work today. With those we can produce a ranked plan and a first evaluation baseline inside 6 weeks. Contact us to start.
Build your generative AI and agentic AI roadmap
Tell us the workflow you want an agent to take on, the systems it must touch and the rules it must respect. We will return an evaluation plan and an honest view of what can be automated safely.