Choice of foundation models
Models from Anthropic, Amazon, Meta, Mistral AI, Cohere, DeepSeek, Stability AI and more behind one consistent API. When a better model arrives, you switch without rewriting your app.
Amazon Bedrock consulting
Generative AI that knows your business. Built on Amazon Bedrock.
Bedrock gives you the leading foundation models through one AWS service, and your data stays private. We help you pick the right first use case, build it on your own documents and systems, and run it in production. No six-month science project.
Amazon Bedrock
Example: an assistant on your policy PDFs
Can a customer return an opened item after 20 days?
Yes, if it is within 30 days of delivery and the item is undamaged. Opened electronics need the original box. Refunds go back to the original payment method.
Returns-Policy.pdf, section 4.2Guardrails on: personal data redacted
- 500+
- customers worldwide
- 50+
- technology alliances
- 24*7
- global delivery centre
- IN + UAE
- offices in India and Dubai
Talk to a Bedrock specialist
Got a gen AI idea? Let's test it on your data.
Tell us the problem you would like gen AI to take on. A person from our AWS team, not a bot, will get back to you within one business day with honest next steps, even if the answer is "not yet".
One API. A whole shelf of models.
- Claude Anthropic
- Nova Amazon
- Titan Amazon
- Llama Meta
- Mistral AI
- Cohere
- DeepSeek
- AI21 Labs
- Stability AI
- and more
What is available differs by AWS Region. We check what runs where your data needs to stay.
What Amazon Bedrock is
It is not a model. It is where you run the best ones, safely.
Think of Amazon Bedrock as a managed front door to foundation models. Claude, Nova, Llama, Mistral and others sit behind one API, so you are not tied to a single vendor and there are no GPU servers for your team to look after.
Around the models, Bedrock adds what a real application needs: Knowledge Bases so answers come from your documents, Guardrails to keep answers safe and on topic, tools to customise and evaluate models, and agents that can take actions in your systems.
The first question every CIO asks: what happens to our data?
Your prompts and outputs are not used to train the base models and are not shared with the model providers. Data is encrypted in transit and at rest, and traffic can stay off the public internet with VPC endpoints and AWS PrivateLink.
Where gen AI earns its keep
- Answering questions from large piles of documents
- First drafts: summaries, replies, product copy
- Pulling fields out of messy contracts, claims and forms
- Search that understands meaning, not just keywords
Where you still want people (or plain code)
- Anything that must be right every time without a check
- Exact maths and calculations, which normal software does better
- Decisions someone has to be accountable for
- Fixing bad data. Out-of-date documents give out-of-date answers
Key capabilities
What is in the box.
Nine building blocks. Most projects use three or four of them. Part of our job is telling you which.
Knowledge Bases (RAG)
Point Bedrock at your documents. It handles ingesting, chunking, embedding and retrieval, and every answer comes back with citations people can check.
Bedrock Guardrails
Block topics you do not want touched, filter harmful content, redact personal data, and check answers against the source with contextual grounding and Automated Reasoning checks.
Model customisation
Fine-tuning, continued pre-training and distillation on your own data, kept private. Worth it when good prompts alone do not get the tone or accuracy right.
Agents & AgentCore
Agents that do things, not just answer: look up an order, raise a ticket, update a record. Amazon Bedrock AgentCore runs them at scale.
Bedrock Data Automation
Turns documents, images, audio and video into structured data. Think invoices, claim forms and call recordings.
Model evaluation
Compare models and RAG pipelines on accuracy and safety, with automatic scoring and human review, before you commit to one.
Prompt management & routing
Version and test prompts like code. Intelligent prompt routing sends each request to the cheapest model that still does the job well.
Flexible inference
Pay per token on demand, run big jobs in batch, or reserve provisioned throughput. Prompt caching and cross-Region inference help with cost and scale.
Use cases by industry
Where it pays off first.
Six places we see Bedrock make a real difference. Most companies start with one and grow from there.
A support assistant that answers from your policy PDFs
Customers ask about orders, returns and plans in their own words, in several languages. The assistant answers from your knowledge base and shows where the answer came from.
KYC, claims and contracts, read for you
Pull out the fields that matter from KYC documents, claims and agreements, summarise them, and hand a clean record to the person who signs off.
Contract review that used to take a week
Compare clauses against your playbook, flag what looks unusual and answer compliance questions with citations, so lawyers spend their time on the judgement calls.
Product copy at catalogue scale
Descriptions, campaign copy and translations for thousands of products, written to your brand guidelines and reviewed before they go live.
Less typing for clinicians
Summarise notes and reports so clinicians spend less time on paperwork. Privacy controls and a human review step are part of the flow, not an afterthought.
An internal helpdesk that knows the policy
Employees ask one place instead of digging through wikis, policy folders and old tickets.
How we work
From idea to production in 4 steps.
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1
Pick the right problem
We sit with your business and IT leads, list the candidate use cases and score them on value, feasibility and risk. You leave with one or two worth building first.
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2
Prove it on your data
We benchmark a few models on your real documents and build a working proof of concept. You see actual answers, not a slide deck.
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3
Build it properly
RAG, agents and integrations engineered for production, with Guardrails, private networking and security testing done before launch, not after.
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4
Run it and keep improving
Our 24*7 delivery centre watches answer quality, usage and cost, and tests every model upgrade before it reaches your users.
ConsultEdge Bedrock services
Ways to work with us.
Pick the one that matches where you are today. Each one ends with something you can use, not just a report.
3 to 4 weeks
Gen AI strategy & proof of concept
For teams with ideas but no clear first move. We find the use cases with the clearest payback and prove one on Bedrock using your data.
- Use case discovery and value scoring
- Model selection and benchmarking
- Rapid PoC on Amazon Bedrock
- Responsible AI and risk review
You walk away with
A working PoC, evaluation results and a roadmap to production.
4 to 8 weeks
RAG & knowledge assistants
Assistants and search that answer from your documents, policies and data with citations, built on Bedrock Knowledge Bases and Amazon OpenSearch.
- Ingestion from S3, SharePoint, Confluence and more
- Chunking, embedding and retrieval tuning
- Access-aware retrieval, so people only see what they are allowed to
- Front ends in the web, Microsoft Teams or Slack
You walk away with
A production assistant with measured answer accuracy.
4 to 10 weeks
Fine-tuning & model customisation
When good prompts are not enough. We fine-tune or distil a model for your domain to improve accuracy, tone or cost.
- Training data preparation and labelling
- Fine-tuning and distillation on Bedrock
- Evaluation against baseline models
- Amazon SageMaker when needs go further
You walk away with
A customised model with documented gains in quality and cost.
2 to 4 weeks
Security, Guardrails & compliance
Our CERT-In empanelled security practice makes your gen AI apps safe, private and ready for audit.
- Bedrock Guardrails configuration
- Private networking with VPC and PrivateLink
- Prompt injection and LLM security testing
- Governance aligned to ISO 27001 and India's DPDP Act
You walk away with
Security architecture, test reports and compliance evidence.
Ongoing
Managed GenAIOps, 24*7
Going live is the start, not the finish. Our Global Delivery Centre keeps your Bedrock apps accurate, reliable and within budget.
- Quality and drift monitoring
- Token usage and cost tuning
- Model upgrades with regression tests
- Incident management against SLAs
You walk away with
A monthly report on quality, usage and cost, and a team on call.
Why ConsultEdge
An AWS partner that has moved gen AI past the demo.
We are an AWS Advanced Tier Partner with the AWS Generative AI and Agentic AI competencies. Our team knows Bedrock, RAG design and AWS security inside out, and we work with enterprises across India, the UAE and beyond to turn experiments into systems people rely on every day.
- AWS Advanced Tier Partner
- AWS Generative AI & Agentic AI competencies
- CERT-In empanelled security practice
- ISO 9001:2015 and ISO 27001:2022
FAQs
Straight answers.
The questions business and tech leads ask us most about Amazon Bedrock.
Ask your own questionAmazon Bedrock is a fully managed AWS service that gives you leading foundation models through one API, plus the tools to build real applications with them: Knowledge Bases for answering from your data, Guardrails, agents, model customisation and evaluation. There are no servers or GPUs for you to manage.
Models from Anthropic (Claude), Amazon (Nova and Titan), Meta (Llama), Mistral AI, Cohere, DeepSeek, AI21 Labs and Stability AI, among others. What is available differs by AWS Region, so we confirm the list for the Region your data needs to stay in.
No. Your prompts and outputs are not used to train the base models and are not shared with the model providers. Data is encrypted in transit and at rest, and you can keep traffic off the public internet with VPC endpoints and AWS PrivateLink.
Retrieval-augmented generation (RAG) means the model looks up relevant passages in your own content before it answers, instead of relying only on what it learned in training. Bedrock Knowledge Bases run the whole pipeline (ingesting, embedding, storing and retrieving) and return citations so people can check the answer.
It can, which is why we design for it. Grounding answers in your documents, using the contextual grounding checks in Guardrails and testing against a set of real questions brings this down a lot, but no one can promise zero. For high-stakes answers we build in citations and a human review step.
Guardrails let you filter harmful content, block topics you do not want discussed, redact sensitive information such as PII, and check that responses are grounded in source data. You set them up once and apply them across models and applications.
Amazon Bedrock gives you the models and the gen AI building blocks. Amazon Bedrock AgentCore is a related set of services for deploying and running AI agents securely at scale, and it works with models on Bedrock or elsewhere.
It is pay-as-you-go. On demand, you pay per input and output token. For bigger workloads there is batch inference or provisioned throughput, and prompt caching and intelligent prompt routing help keep the bill down. Every engagement with us includes a cost estimate before you commit.
